# ML Papers of The Week

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At DAIR.AI we ❤️ reading ML papers so we've created this repo to highlight the top ML papers of every week.

Here is the weekly series:

- [Top ML Papers of the Week (December 18 - December 24)](./#top-ml-papers-of-the-week-december-25---december-31)
- [Top ML Papers of the Week (December 18 - December 24)](./#top-ml-papers-of-the-week-december-18---december-24)
- [Top ML Papers of the Week (December 11 - December 17)](./#top-ml-papers-of-the-week-december-11---december-17)
- [Top ML Papers of the Week (December 4 - December 10)](./#top-ml-papers-of-the-week-december-4---december-10)
- [Top ML Papers of the Week (November 27 - December 3)](./#top-ml-papers-of-the-week-november-27---december-3)
- [Top ML Papers of the Week (November 20 - November 26)](./#top-ml-papers-of-the-week-november-20---november-26)
- [Top ML Papers of the Week (November 13 - November 19)](./#top-ml-papers-of-the-week-november-13---november-19)
- [Top ML Papers of the Week (November 6 - November 12)](./#top-ml-papers-of-the-week-november-6---november-12)
- [Top ML Papers of the Week (October 30 - November 5)](./#top-ml-papers-of-the-week-october-30---november-5)
- [Top ML Papers of the Week (October 23 - October 29)](./#top-ml-papers-of-the-week-october-23---october-29)
- [Top ML Papers of the Week (October 16 - October 22)](./#top-ml-papers-of-the-week-october-16---october-22)
- [Top ML Papers of the Week (October 9 - October 15)](./#top-ml-papers-of-the-week-october-9---october-15)
- [Top ML Papers of the Week (October 2 - October 8)](./#top-ml-papers-of-the-week-october-2---october-8)
- [Top ML Papers of the Week (September 25 - October 1)](./#top-ml-papers-of-the-week-september-25---october-1)
- [Top ML Papers of the Week (September 18 - September 24)](./#top-ml-papers-of-the-week-september-18---september-24)
- [Top ML Papers of the Week (September 11 - September 17)](./#top-ml-papers-of-the-week-september-11---september-17)
- [Top ML Papers of the Week (September 4 - September 10)](./#top-ml-papers-of-the-week-september-4---september-10)
- [Top ML Papers of the Week (August 28 - September 3)](./#top-ml-papers-of-the-week-august-28---september-3)
- [Top ML Papers of the Week (August 21 - August 27)](./#top-ml-papers-of-the-week-august-21---august-27)
- [Top ML Papers of the Week (August 14 - August 20)](./#top-ml-papers-of-the-week-august-14---august-20)
- [Top ML Papers of the Week (August 7 - August 13)](./#top-ml-papers-of-the-week-august-7---august-13)
- [Top ML Papers of the Week (July 31 - August 6)](./#top-ml-papers-of-the-week-july-31---august-6)
- [Top ML Papers of the Week (July 24 - July 30)](./#top-ml-papers-of-the-week-july-24---july-30)
- [Top ML Papers of the Week (July 17 - July 23)](./#top-ml-papers-of-the-week-july-17---july-23)
- [Top ML Papers of the Week (July 10 - July 16)](./#top-ml-papers-of-the-week-july-10---july-16)
- [Top ML Papers of the Week (July 3 - July 9)](./#top-ml-papers-of-the-week-july-3---july-9)
- [Top ML Papers of the Week (June 26 - July 2)](./#top-ml-papers-of-the-week-june-26---july-2)
- [Top ML Papers of the Week (June 19 - June 25)](./#top-ml-papers-of-the-week-june-19---june-25)
- [Top ML Papers of the Week (June 12 - June 18)](./#top-ml-papers-of-the-week-june-12---june-18)
- [Top ML Papers of the Week (June 5 - June 11)](./#top-ml-papers-of-the-week-june-5---june-11)
- [Top ML Papers of the Week (May 29 - June 4)](./#top-ml-papers-of-the-week-may-29-june-4)
- [Top ML Papers of the Week (May 22 - 28)](./#top-ml-papers-of-the-week-may-22-28)
- [Top ML Papers of the Week (May 15 - 21)](./#top-ml-papers-of-the-week-may-15-21)
- [Top ML Papers of the Week (May 8 - 14)](./#top-ml-papers-of-the-week-may-8-14)
- [Top ML Papers of the Week (May 1-7)](./#top-ml-papers-of-the-week-may-1-7)
- [Top ML Papers of the Week (April 24 - April 30)](./#top-ml-papers-of-the-week-april-24---april-30)
- [Top ML Papers of the Week (April 17 - April 23)](./#top-ml-papers-of-the-week-april-17---april-23)
- [Top ML Papers of the Week (April 10 - April 16)](./#top-ml-papers-of-the-week-april-10---april-16)
- [Top ML Papers of the Week (April 3 - April 9)](./#top-ml-papers-of-the-week-april-3---april-9)
- [Top ML Papers of the Week (Mar 27 - April 2)](./#top-ml-papers-of-the-week-mar-27---april-2)
- [Top ML Papers of the Week (Mar 20-Mar 26)](./#top-ml-papers-of-the-week-mar-20-mar-26)
- [Top ML Papers of the Week (Mar 13-Mar 19)](./#top-ml-papers-of-the-week-mar-13-mar-19)
- [Top ML Papers of the Week (Mar 6-Mar 12)](./#top-ml-papers-of-the-week-mar-6-mar-12)
- [Top ML Papers of the Week (Feb 27-Mar 5)](./#top-ml-papers-of-the-week-feb-27-mar-5)
- [Top ML Papers of the Week (Feb 20-26)](./#top-ml-papers-of-the-week-feb-20-26)
- [Top ML Papers of the Week (Feb 13 - 19)](./#top-ml-papers-of-the-week-feb-13---19)
- [Top ML Papers of the Week (Feb 6 - 12)](./#top-ml-papers-of-the-week-feb-6---12)
- [Top ML Papers of the Week (Jan 30-Feb 5)](./#top-ml-papers-of-the-week-jan-30-feb-5)
- [Top ML Papers of the Week (Jan 23-29)](./#top-ml-papers-of-the-week-jan-23-29)
- [Top ML Papers of the Week (Jan 16-22)](./#top-ml-papers-of-the-week-jan-16-22)
- [Top ML Papers of the Week (Jan 9-15)](./#top-ml-papers-of-the-week-jan-9-15)
- [Top ML Papers of the Week (Jan 1-8)](./#top-ml-papers-of-the-week-jan-1-8)

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---
## Top ML Papers of the Week (December 25 - December 31)
| **Paper**  | **Links** |
| 1) **CogAgent** - presents an 18 billion parameter visual language model specializing in GUI understanding and navigation; supports high-resolution inputs (1120x1120) and shows abilities in tasks such as visual Q&A, visual grounding, and GUI Agent; achieves state of the art on 5 text-rich and 4 general VQA benchmarks. | [Paper](https://arxiv.org/abs/2312.08914), [Tweet](https://x.com/cenyk1230/status/1739916469272789222?s=20) |
| 2) **From Gemini to Q-Star** - surveys 300+ papers and summarizes research developments to look at in the space of Generative AI; it covers computational challenges, scalability, real-world implications, and the potential for Gen AI to drive progress in fields like healthcare, finance, and education. | [Paper](https://arxiv.org/abs/2312.10868), [Tweet](https://x.com/omarsar0/status/1740119485011390558?s=20) |
| 3) **PromptBench** - a unified library that supports comprehensive evaluation and analysis of LLMs; it consists of functionalities for prompt construction, prompt engineering, dataset and model loading, adversarial prompt attack, dynamic evaluation protocols, and analysis tools. | [Paper](https://arxiv.org/abs/2312.07910v1), [Tweet](https://x.com/omarsar0/status/1739360426134028631?s=20) |
| 4) **Exploiting Novel GPT-4 APIs** - performs red-teaming on three functionalities exposed in the GPT-4 APIs: fine-tuning, function calling, and knowledge retrieval; Main findings: 1) fine-tuning on as few as 15 harmful examples or 100 benign examples can remove core safeguards from GPT-4, 2) GPT-4 Assistants divulge the function call schema and can be made to execute arbitrary function calls, and 3) knowledge retrieval can be hijacked by injecting instructions into retrieval documents. | [Paper](https://arxiv.org/abs/2312.14302), [Tweet](https://x.com/omarsar0/status/1739677995747450964?s=20) |
| 5) **Fact Recalling in LLMs** - investigates how MLP layers implement a lookup table for factual recall; scopes the study on how early MLPs in Pythia 2.8B look up which of 3 different sports various athletes play; suggests that early MLP layers act as a lookup table and recommends thinking about the recall of factual knowledge in the model as multi-token embeddings.  | [Paper](https://www.alignmentforum.org/s/hpWHhjvjn67LJ4xXX/p/iGuwZTHWb6DFY3sKB), [Tweet](https://x.com/NeelNanda5/status/1738559368361349122?s=20) |
| 6) **Generative AI for Math** - presents a diverse and high-quality math-centric corpus comprising of ~9.5 billion tokens to train foundation models. | [Paper](https://arxiv.org/abs/2312.17120), [Tweet](https://x.com/arankomatsuzaki/status/1740564961032556942?s=20) |
| 7) **Pricipled Instructions Are All You Need** - introduces 26 guiding principles designed to streamline the process of querying and prompting large language models; applies these principles to conduct extensive experiments on LLaMA-1/2 (7B, 13B and 70B), GPT-3.5/4 to verify their effectiveness on instructions and prompts design. | [Paper](https://arxiv.org/abs/2312.16171v1), [Tweet](https://x.com/_akhaliq/status/1739857456161759455?s=20) |
| 8) **A Survey of Reasoning with Foundation Models** - provides a comprehensive survey of seminal foundational models for reasoning, highlighting the latest advancements in various reasoning tasks, methods, benchmarks, and potential future directions; also discusses how other developments like multimodal learning, autonomous agents, and super alignment accelerate and extend reasoning research. | [Paper](https://arxiv.org/abs/2312.11562v4), [Tweet](https://x.com/omarsar0/status/1740729489661874632?s=20) |
| 9) **Making LLMs Better at Dense Retrieval** - proposes LLaRA which adapts an LLM for dense retrieval; it consists of two pretext tasks: EBAE (Embedding-Based Auto-Encoding) and EBAR (Embedding-Based Auto-Regression), where the text embeddings from LLM are used to reconstruct the tokens for the input sentence and predict the tokens for the next sentence, respectively; a LLaMa-2-7B was improved on benchmarks like MSMARCO and BEIR. | [Paper](https://arxiv.org/abs/2312.15503v1) |
| 10) **Gemini vs GPT-4V - provides a comprehensive preliminary comparison and combination of vision-language models like Gemini and GPT-4V through several qualitative cases; finds that GPT-4V is precise and succinct in responses, while Gemini excels in providing detailed, expansive answers accompanied by relevant imagery and links.  | [Paper](https://arxiv.org/abs/2312.15011v1), [Tweet](https://x.com/omarsar0/status/1741177994377330895?s=20) |

---
## Top ML Papers of the Week (December 18 - December 24)
| **Paper**  | **Links** |
| ------------- | ------------- |
| 1) **Gemini’s Language Abilities** - provides an impartial and reproducible study comparing several popular models like Gemini, GPT, and Mixtral; Gemini Pro achieves comparable but slightly lower accuracy than the current version of GPT 3.5 Turbo; Gemini and GPT were better than Mixtral. | [Paper](https://arxiv.org/abs/2312.11444), [Tweet](https://x.com/gneubig/status/1737108966931673191?s=20)|
| 2) **PowerInfer** - a high-speed inference engine for deploying LLMs locally; exploits the high locality in LLM inference to design a GPU-CPU hybrid inference engine; hot-activated neurons are preloaded onto the GPU for fast access, while cold-activated neurons (the majority) are computed on the CPU; this approach significantly reduces GPU memory demands and CPU-GPU data transfer. | [Paper](https://ipads.se.sjtu.edu.cn/_media/publications/powerinfer-20231219.pdf), [Tweet](https://x.com/omarsar0/status/1737168751668187229?s=20)|
| 3) **Discovery of a New Family of Antibiotics with Graph Deep Learning** - discovered a new structural class of antibiotics with explainable graph algorithms; the approach enables explainable deep learning guided discovery of structural classes of antibiotics which helps to provide chemical substructures that underlie antibiotic activity. | [Paper](https://www.nature.com/articles/s41586-023-06887-8), [Tweet](https://x.com/EricTopol/status/1737505177052348545?s=20)|
| 4) **VideoPoet** - introduces a large language model for zero-shot video generation; it’s capable of a variety of video generation tasks such as image-to-video and video stylization; trains an autoregressive model to learn across video, image, audio, and text modalities by using multiple tokenizers; shows that language models can synthesize and edit video with some degree of temporal consistency. | [Paper](https://sites.research.google/videopoet/), [Tweet](https://x.com/GoogleAI/status/1737235593078456389?s=20)_|
| 5) **Multimodal Agents as Smartphone Users** - introduces an LLM-based multimodal agent framework to operate smartphone applications; learns to navigate new apps through autonomous exploration or observing human demonstrations; shows proficiency in handling diverse tasks across different applications like email, social media, shopping, editing tools, and more. | [Paper](https://arxiv.org/abs/2312.13771), [Tweet](https://x.com/omarsar0/status/1738265651188253051?s=20)_|
| 6) **LLM in a Flash** - proposes an approach that efficiently runs LLMs that exceed the available DRAM capacity by storing the model parameters on flash memory but bringing them on demand to DRAM; enables running models up to twice the size of the available DRAM, with a 4-5x and 20-25x increase in inference speed compared to naive loading approaches in CPU and GPU, respectively. | [Paper](https://arxiv.org/abs/2312.11514), [Tweet](https://x.com/gabrielnocode/status/1737307286887133552?s=20)_|
| 7) **ReST Meets ReAct** - proposes a ReAct-style agent with self-critique for improving on the task of long-form question answering; it shows that the agent can be improved through ReST-style (reinforced self-training) iterative fine-tuning on its reasoning traces; specifically, it uses growing-batch RL with AI feedback for continuous self-improvement and self-distillation; like a few other recent papers, it focuses on minimizing human involvement (i.e., doesn't rely on human-labeled training data); it generates synthetic data with self-improvement from AI feedback which can then be used to distill the agent into smaller models (1/2 orders magnitude) with comparable performance as the pre-trained agent. | [Paper](https://arxiv.org/abs/2312.10003), [Tweet](https://x.com/omarsar0/status/1736587397830176910?s=20)_|
| 8) **Adversarial Attacks on GPT-4** - uses a simple random search algorithm to implement adversarial attacks on GPT-4; it achieves jailbreaking by appending an adversarial suffix to an original request, then iteratively making slight random changes to the suffix, and keeping changes if it increases the log probability of the token “Sure” at the first position of the response. | [Paper](https://www.andriushchenko.me/gpt4adv.pdf), [Tweet](https://x.com/maksym_andr/status/1737844601891983563?s=20)_|
| 9) **RAG for LLMs** - an overview of all the retrieval augmented generation (RAG) research that has been happening. | [Paper](https://arxiv.org/abs/2312.10997v1), [Tweet](https://x.com/omarsar0/status/1738354427759612222?s=20)_|
| 10) **Findings of the BabyLLM Challenge** - presents results for a new challenge that involves sample-efficient pretraining on a developmentally plausible corpus; the winning submission, which uses flashy LTG BERT, beat Llama 2 70B on 3/4 evals; other approaches that saw good results included data preprocessing or training on shorter context.  | [Paper](https://aclanthology.org/volumes/2023.conll-babylm/), [Tweet](https://x.com/a_stadt/status/1737849248560066794?s=20)_|

---
## Top ML Papers of the Week (December 11 - December 17)
| **Paper**  | **Links** |
| ------------- | ------------- |
| 1) **LLMs for Discoveries in Mathematical Sciences** - uses LLMs to search for new solutions in mathematics & computer science; proposes FunSearch which combines a pre-trained LLM with a systematic evaluator and iterates over them to evolve low-scoring programs into high-scoring ones discovering new knowledge; one of the key findings in this work is that safeguarding against LLM hallucinations is important to produce mathematical discoveries and other real-world problems. | [Paper](https://www.nature.com/articles/s41586-023-06924-6), [Tweet](https://x.com/GoogleDeepMind/status/1735332722208284797?s=20) |
| 2) **Weak-to-strong Generalization** - studies whether weak model supervision can elicit the full capabilities of stronger models; finds that when naively fine-tuning strong pretrained models on weak model generated labels they can perform better than their weak supervisors; reports that finetuning GPT-4 with a GPT-2-level supervisor it’s possible to recover close to GPT-3.5-level performance on NLP tasks. | [Paper](https://cdn.openai.com/papers/weak-to-strong-generalization.pdf), [Tweet](https://x.com/OpenAI/status/1735349718765715913?s=20) |
| 3) **Audiobox** - a unified model based on flow-matching capable of generating various audio modalities; designs description-based and example-based prompting to enhance controllability and unify speech and sound generation paradigms; adapts a self-supervised infilling objective to pre-train on large quantities of unlabeled audio; performs well on speech and sound generation and unlocks new methods for generating audio with novel vocal and acoustic styles. | [Paper](https://ai.meta.com/research/publications/audiobox-unified-audio-generation-with-natural-language-prompts/), [Tweet](https://x.com/AIatMeta/status/1734257634008531453?s=20) |
| 4) **Mathematical LLMs** - a survey on the progress of LLMs on mathematical tasks; covers papers and resources on LLM research around prompting techniques and tasks such as math word problem-solving and theorem proving. | [Paper](https://arxiv.org/abs/2312.07622), [Tweet](https://x.com/omarsar0/status/1735323577392542084?s=20) |
| 5) **Towards Fully Transparent Open-Source LLMs** - proposes LLM360 to support open and collaborative AI research by making the end-to-end LLM training process transparent and reproducible; releases 7B parameter LLMs pre-trained from scratch, AMBER and CRYSTALCODER, including their training code, data, intermediate checkpoints, and analyses.  | [Paper](https://arxiv.org/abs/2312.06550), [Tweet](https://x.com/omarsar0/status/1734591071575744820?s=20) |
| 6) **LLMs in Medicine** - a comprehensive survey (analyzing 300+ papers) on LLMs in medicine; includes an overview of the principles, applications, and challenges faced by LLMs in medicine.  | [Paper](https://arxiv.org/abs/2311.05112), [Tweet](https://x.com/omarsar0/status/1734599425568231513?s=20) |
| 7) **Beyond Human Data for LLMs** - proposes an approach for self-training with feedback that can substantially reduce dependence on human-generated data; the model-generated data combined with a reward function improves the performance of LLMs on problem-solving tasks. | [Paper](https://arxiv.org/abs/2312.06585), [Tweet](https://x.com/omarsar0/status/1734953578274386002?s=20) |
| 8) **Gaussian-SLAM** - a neural RGBD SLAM method capable of photorealistically reconstructing real-world scenes without compromising speed and efficiency; extends classical 3D Gaussians for scene representation to overcome the limitations of the previous methods. | [Paper](https://vladimiryugay.github.io/gaussian_slam/), [Tweet](https://x.com/vlyug/status/1734683948440252480?s=20) |
| 9) **Pearl** - introduces a new production-ready RL agent software package that enables researchers and practitioners to develop RL AI agents that adapt to environments with limited observability, sparse feedback, and high stochasticity.  | [Paper](https://arxiv.org/abs/2312.03814), [Tweet](https://x.com/ZheqingZhu/status/1732880717263352149?s=20) |
| 10) **Quip** - compresses trained model weights into a lower precision format to reduce memory requirements; the approach combines lattice codebooks with incoherence processing to create 2 bit quantized models; significantly closes the gap between 2 bit quantized LLMs and unquantized 16 bit models. | [Paper](https://cornell-relaxml.github.io/quip-sharp/), [Tweet](https://x.com/tsengalb99/status/1733222467953422702?s=20) |

---
## Top ML Papers of the Week (December 4 - December 10)
| **Paper**  | **Links** |
| ------------- | ------------- |
| 1) **Gemini** - a series of multimodal models with multimodal reasoning capabilities across text, images, video, audio, and code; claims to outperform human experts on MMLU, a popular benchmark to test the knowledge and problem-solving abilities of AI models; capabilities reported include multimodality, multilinguality, factuality, summarization, math/science, long-context, reasoning, and more. | [Paper](https://storage.googleapis.com/deepmind-media/gemini/gemini_1_report.pdf), [Tweet](https://x.com/omarsar0/status/1732434324291563831?s=20) |
| 2) **EfficientSAM** - a lightweight Segment Anything Model (SAM) that exhibits decent performance with largely reduced complexity; leverages masked autoencoders with 20x fewer parameters and 20x faster runtime; EfficientSAM performs within 2 points (44.4 AP vs 46.5 AP) of the original SAM model.| [Paper](https://arxiv.org/abs/2312.00863), [Tweet](https://x.com/fiandola/status/1732171016783180132?s=20)  |
| 3) **Magicoder** - a series of fully open-source LLMs for code that close the gap with top code models while having no more than 7B parameters; trained on 75K synthetic instruction data; uses open-source references for the production of more diverse, realistic, high-quality, and controllable data; outperforms state-of-the-art code models with similar or even larger sizes on several coding benchmarks, including Python text-to-code generation, multilingual coding, and data-science program completion; MagicoderS-CL-7B based on CodeLlama surpasses ChatGPT on HumanEval+ (66.5 vs. 65.9 in pass@1).| [Paper](https://arxiv.org/abs/2312.02120), [Tweet](https://x.com/omarsar0/status/1732063926613946863?s=20)  |
| 4) **LLMs on Graphs** - a comprehensive overview that summarizes different scenarios where LLMs are used on graphs such as pure graphs, text-rich graphs, and text-paired graphs | [Paper](https://arxiv.org/abs/2312.02783), [Tweet](https://x.com/omarsar0/status/1732404393037762588?s=20)  |
| 5) **Llama Guard** - an LLM-based safeguard model that involves a small (Llama2-7B) customizable instruction-tuned model that can classify safety risks in prompts and responses for conversational AI agent use cases; the model can be leveraged in a zero-shot or few-shot way if you need to adapt it to a different safety risk taxonomy that meets the requirements for a target use case; it can also be fine-tune on a specific dataset to adapt to a new taxonomy.  | [Paper](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/), [Tweet](https://x.com/omarsar0/status/1732781628139696279?s=20)  |
| 6) **Human-Centered Loss Functions** - proposes an approach called Kahneman-Tversky Optimization (KTO) that matches or exceeds DPO performance methods at scales from 1B to 30B; KTO maximizes the utility of LLM generations instead of maximizing the log-likelihood of preferences as most current methods do. | [Paper](https://github.com/ContextualAI/HALOs/blob/main/assets/report.pdf), [Tweet](https://x.com/ethayarajh/status/1732837520784957476?s=20)  |
| 7) **Chain of Code** - a simple extension of the chain-of-thought approach that improves LM code-driven reasoning; it encourages LMs to format semantic sub-tasks in a program as pseudocode that the interpreter can explicitly catch undefined behavior and hand off to simulate with an LLM; on BIG-Bench Hard, Chain of Code achieves 84%, a gain of 12% over Chain of Thought. | [Paper](https://arxiv.org/abs/2312.04474), [Tweet](https://x.com/ChengshuEricLi/status/1733169631949701425?s=20)  |
| 8) **Data Management For LLMs** - an overview of current research in data management within both the pretraining and supervised fine-tuning stages of LLMs; it covers different aspects of data management strategy design: data quantity, data quality, domain/task composition, and more. | [Paper](https://arxiv.org/abs/2312.01700), [Tweet](https://x.com/omarsar0/status/1731877232493166969?s=20)  |
| 9) *8RankZephyr** - an open-source LLM for listwise zero-shot reranking that bridges the effectiveness gap with GPT-4 and in some cases surpasses the proprietary model; it outperforms GPT-4 on the NovelEval test set, comprising queries and passages past its training period, which addresses concerns about data contamination. | [Paper](https://arxiv.org/abs/2312.02724), [Tweet](https://x.com/lintool/status/1732430269485867114?s=20)  |
| 10)  **The Efficiency Spectrum of LLMs** - a comprehensive review of algorithmic advancements aimed at improving LLM efficiency; covers various topics related to efficiency, including scaling laws, data utilization, architectural innovations, training and tuning strategies, and inference techniques. | [Paper](https://arxiv.org/abs/2312.00678), [Tweet](https://x.com/omarsar0/status/1731696419457606048?s=20)  |

---
## Top ML Papers of the Week (November 27 - December 3)
| **Paper**  | **Links** |
| ------------- | ------------- |
| 1) **GNoME** - a new AI system for material design that finds 2.2 million new crystals, including 380,000 stable materials; presents a new deep learning tool that increases the speed and efficiency of discovery by predicting the stability of new materials. | [Paper](https://www.nature.com/articles/s41586-023-06735-9), [Tweet](https://x.com/demishassabis/status/1729995611443769823?s=20) |
| 2) **Open-Source LLMs vs. ChatGPT** - provides an exhaustive overview of tasks where open-source LLMs claim to be on par or better than ChatGPT. | [Paper](https://arxiv.org/abs/2311.16989), [Tweet](https://x.com/sophiamyang/status/1730108858889097710?s=20) |
| 3) **Adversarial Diffusion Distillation** - a novel training approach that efficiently samples large-scale foundation image diffusion models in just 1-4 steps while maintaining high image quality; combines score distillation and an adversarial loss to ensure high image fidelity even in the low-step regime of one or two sampling steps; reaches performance of state-of-the-art diffusion models in only four steps. | [Paper](https://stability.ai/research/adversarial-diffusion-distillation), [Tweet](https://x.com/robrombach/status/1729590281647870342?s=20) |
| 4) **Seamless** - a family of research models that enable end-to-end expressive cross-lingual communication in a streaming fashion; introduces an improved SeamlssM4T model trained on more low-resource language data; also applies red-teaming effort for safer multimodal machine translation. | [Paper](https://ai.meta.com/research/publications/seamless-multilingual-expressive-and-streaming-speech-translation/), [Tweet](https://x.com/AIatMeta/status/1730294284023427221?s=20) |
| 5) **MEDITRON-70B** - a suite of open-source LLMs with 7B and 70B parameters adapted to the medical domain; builds on Llama-2 and extends pretraining on a curated medical corpus; MEDITRON-70B outperforms GPT-3.5 and Med-PaLM and is within 5% of GPT-4 and 10% of Med-PaLM-2.  | [Paper](https://arxiv.org/abs/2311.16079v1), [Tweet](https://x.com/eric_zemingchen/status/1729563855213175010?s=20) |
| 6) **Foundation Models Outcompeting Special-Purpose Tuning** - performs a systematic exploration of prompt engineering to boost the performance of LLMs on medical question answering; uses prompt engineering methods that are general purpose and make no use of domain expertise; prompt engineering led to enhancing GPT-4’s performance and achieves state-of-the-art results on nine benchmark datasets in the MultiMedQA suite. | [Paper](https://arxiv.org/abs/2311.16452), [Tweet](https://x.com/erichorvitz/status/1729854235443884385?s=20) |
| 7) **UniIR** - a unified instruction-guided multimodal retriever that handles eight retrieval tasks across modalities; can generalize to unseen retrieval tasks and achieves robust performance across existing datasets and zero-shot generalization to new tasks; presents a multimodal retrieval benchmark to help standardize the evaluation of multimodal information retrieval. | [Paper](https://arxiv.org/abs/2311.17136), [Tweet](https://x.com/CongWei1230/status/1730307767469068476?s=20) |
| 8) **Safe Deployment of Generative AI** - argues that to protect people’s privacy, medical professionals, not commercial interests, must drive the development and deployment of such models. | [Paper](https://www.nature.com/articles/d41586-023-03803-y), [Tweet](https://x.com/ClementDelangue/status/1730300666403238393?s=20) |
| 9) **On Bringing Robots Home** - introduces Dobb-E, an affordable and versatile general-purpose system for learning robotic manipulation within household settings; Dobbe-E can learn new tasks with only 5 minutes of user demonstrations; experiments reveal unique challenges absent or ignored in lab robotics, including effects of strong shadows, variable demonstration quality by non-expert users, among others. | [Paper](https://arxiv.org/abs/2311.16098v1), [Tweet](https://x.com/LerrelPinto/status/1729515379892826211?s=20) |
| 10) **Translatotron 3** - proposes an unsupervised approach to speech-to-speech translation that can learn from monolingual data alone; combines masked autoencoder, unsupervised embedding mapping, and back-translation; results show that the model outperforms a baseline cascade system and showcases its capability to retain para-/non-linguistic such as pauses, speaking rates, and speaker identity. | [Paper](https://arxiv.org/abs/2305.17547), [Tweet](https://x.com/GoogleAI/status/1730654297350959413?s=20) |

---
## Top ML Papers of the Week (November 20 - November 26)
| **Paper**  | **Links** |
| ------------- | ------------- |
| 1) **System 2 Attention** - leverages the reasoning and instruction following capabilities of LLMs to decide what to attend to; it regenerates input context to only include relevant portions before attending to the regenerated context to elicit the final response from the model; increases factuality and outperforms standard attention-based LLMs on tasks such as QA and math world problems. | [Paper](https://arxiv.org/abs/2311.11829), [Tweet](https://x.com/jaseweston/status/1726784511357157618?s=20) |
| 2) **Advancing Long-Context LLMs** - an overview of the methodologies for enhancing Transformer architecture modules that optimize long-context capabilities across all stages from pre-training to inference. | [Paper](https://arxiv.org/abs/2311.12351), [Tweet](https://x.com/omarsar0/status/1727358484360945750?s=20) |
| 3) **Parallel Speculative Sampling** - approach to reduce inference time of LLMs based on a variant of speculative sampling and parallel decoding; achieves significant speed-ups (up to 30%) by only learning as little as O(d_emb) additional parameters. | [Paper](https://arxiv.org/abs/2311.13581), [Tweet](https://x.com/omarsar0/status/1728066181796418009?s=20) |
| 4) **Mirasol3B** - a multimodal model for learning across audio, video, and text which decouples the multimodal modeling into separate, focused autoregressive models; the inputs are processed according to the modalities; this approach can handle longer videos compared to other models and it outperforms state-of-the-art approach on video QA, long video QA, and audio-video-text benchmark. | [Paper](https://arxiv.org/abs/2311.05698), [Tweet](https://x.com/GoogleAI/status/1724553024088191211?s=20) |
| 5) **Teaching Small LMs To Reason** - proposes an approach to teach smaller language models to reason; specifically, the LM is thought to use reasoning techniques, such as step-by-step processing, recall-then-generate, recall-reason-generate, extract-generate, and direct-answer methods; outperforms models of similar size and attains performance levels similar or better to those of models 5-10x larger, as assessed on complex tasks that test advanced reasoning abilities in zero-shot settings.| [Paper](https://arxiv.org/abs/2311.11045), [Tweet](https://x.com/omarsar0/status/1726990087399915995?s=20) |
| 6) **GPQA** - proposes a graduate-level Google-proof QA benchmark consisting of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry; the strongest GPT-4 based baseline achieves 39% accuracy; this benchmark offers scalable oversight experiments that can help obtain reliable and truthful information from modern AI systems that surpass human capabilities.| [Paper](https://arxiv.org/abs/2311.12022), [Tweet](https://x.com/idavidrein/status/1727033002234909060?s=20) |
| 7) **The Hitchhiker’s Guide From Chain-of-Thought Reasoning to Language Agents** - summary of CoT reasoning, foundational mechanics underpinning CoT techniques, and their application to language agent frameworks. | [Paper](https://arxiv.org/abs/2311.11797), [Tweet](https://x.com/omarsar0/status/1726803725220487277?s=20) |
| 8) **GAIA** - a benchmark for general AI assistants consisting of real-world questions that require a set of fundamental abilities such as reasoning, multimodal handling, web browsing, and generally tool-use proficiency; shows that human respondents obtain 92% vs. 15% for GPT-4 equipped with plugins. | [Paper](https://arxiv.org/abs/2311.12983), [Tweet](https://x.com/ThomasScialom/status/1727683993045201339?s=20) |
| 9) **LLMs as Collaborators for Medical Reasoning** - proposes a collaborative multi-round framework for the medical domain that leverages role-playing LLM-based agents to enhance LLM proficiency and reasoning capabilities.  | [Paper](https://arxiv.org/abs/2311.10537), [Tweet](https://x.com/omarsar0/status/1726627951582511135?s=20) |
| 10) **TÜLU 2** - presents a suite of improved TÜLU models for advancing the understanding and best practices of adapting pretrained language models to downstream tasks and user preferences; TÜLU 2 suite achieves state-of-the-art performance among open models and matches or exceeds the performance of GPT-3.5-turbo-0301 on several benchmarks. | [Paper](https://arxiv.org/abs/2311.10702), [Tweet](https://x.com/natolambert/status/1727350301131518454?s=20) |

---
## Top ML Papers of the Week (November 13 - November 19)
| **Paper**  | **Links** |
| ------------- | ------------- |
| 1) **Emu Video and Emu Edit** - present new models for controlled image editing and text-to-video generation based on diffusion models; Emu Video can generate high-quality video by using text-only, image-only, or combined text and image inputs; Emu Edit enables free-form editing through text instructions. | [Paper](https://ai.meta.com/blog/emu-text-to-video-generation-image-editing-research/), [Tweet](https://x.com/AIatMeta/status/1725184026154349007?s=20) |
| 2) **Chain-of-Note** - an approach to improve the robustness and reliability of retrieval-augmented language models in facing noisy, irrelevant documents and in handling unknown scenarios; CoN generates sequential reading notes for the retrieved documents, enabling an evaluation of their relevance to the given question and integrating this information to formulate the final answer; CoN significantly outperforms standard retrieval-augmented language models and achieves an average improvement of +7.9 in EM score given entirely noisy retrieved documents and +10.5 in rejection rates for real-time questions that fall outside the pre-training knowledge scope. | [Paper](https://arxiv.org/abs/2311.09210), [Tweet](https://x.com/omarsar0/status/1725181141693472959?s=20) |
| 3) **LLMs for Scientific Discovery** - explores the impact of large language models, particularly GPT-4, across various scientific fields including drug discovery, biology, and computational chemistry; assesses GPT-4's understanding of complex scientific concepts, its problem-solving capabilities, and its potential to advance scientific research through expert-driven case assessments and benchmark testing. | [Paper](https://arxiv.org/abs/2311.07361), [Tweet](https://x.com/omarsar0/status/1724465107046940893?s=20) |
| 4) **Fine-Tuning LLMs for Factuality** - fine-tunes language model for factuality without requiring human labeling; it learns from automatically generated factuality preference rankings and targets open-ended generation settings; it significantly improves the factuality of Llama-2 on held-out topics compared with RLHF or decoding strategies targeted at factuality. | [Paper](https://arxiv.org/abs/2311.08401), [Tweet](https://x.com/arankomatsuzaki/status/1724613041155608951?s=20) |
| 5) **Contrastive CoT Prompting** - proposes a contrastive chain of thought method to enhance language model reasoning; the approach provides both valid and invalid reasoning demonstrations, to guide the model to reason step-by-step while reducing reasoning mistakes; also proposes an automatic method to construct contrastive demonstrations and demonstrates improvements over CoT prompting. | [Paper](https://arxiv.org/abs/2311.09277), [Tweet](https://x.com/arankomatsuzaki/status/1725340150819905723?s=20) |
| 6) **A Survey on Language Models for Code** - provides an overview of LLMs for code, including a review of 50+ models, 30+ evaluation tasks, and 500 related works. | [Paper](https://arxiv.org/abs/2311.07989v1), [Tweet](https://x.com/omarsar0/status/1725637165256761553?s=20) |
| 7) **JARVIS-1** - an open-world agent that can perceive multimodal input | [Paper](https://arxiv.org/abs/2311.05997), [Tweet](https://x.com/arankomatsuzaki/status/1723882043514470629?s=20) |
| 8) **Learning to Filter Context for RAG** - proposes a method that improves the quality of the context provided to the generator via two steps: 1) identifying useful context based on lexical and information-theoretic approaches, and 2) training context filtering models that can filter retrieved contexts at inference; outperforms existing approaches on extractive question answering | [Paper](https://arxiv.org/abs/2311.08377v1), [Tweet](https://x.com/ZhiruoW/status/1724792850079252886?s=20) |
| 9) **MART** - proposes an approach for improving LLM safety with multi-round automatic red-teaming; incorporates automatic adversarial prompt writing and safe response generation, which increases red-teaming scalability and the safety of LLMs; violation rate of an LLM with limited safety alignment reduces up to 84.7% after 4 rounds of MART, achieving comparable performance to LLMs with extensive adversarial prompt writing. | [Paper](https://arxiv.org/abs/2311.07689), [Tweet](https://x.com/AIatMeta/status/1724887918685425829?s=20) |
| 10) **LLMs can Deceive Users** - explores the use of an autonomous stock trading agent powered by LLMs; finds that the agent acts upon insider tips and hides the reason behind the trading decision; shows that helpful and safe LLMs can strategically deceive users in a realistic situation without direction instructions or training for deception. | [Paper](https://arxiv.org/abs/2311.07590), [Tweet](https://x.com/ESYudkowsky/status/1725226563992715521?s=20) |

---

## Top ML Papers of the Week (November 6 - November 12)

| **Paper**                                                                                                                                                                                                                                                                                                                                                                                                                                                                  | **Links**                                                                                                         |
| -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- |
| 1) **Hallucination in LLMs** - a comprehensive survey                                                                                                                                                                                                                                                                                                                                                                                                                      | [Paper](https://arxiv.org/abs/2311.05232), [Tweet](https://x.com/omarsar0/status/1722985251129966705?s=20)        |
| 2) **Simplifying Transformer Blocks** - explores simplifying the transformer block and finds that many block components can be removed with no loss of training speed; using different architectures like autoregressive decoder-only and BERT encoder-only models, the simplified blocks emulate per-update training speed and performance of standard transformers, and even achieve 15% faster training throughput with fewer parameters                                | [Paper](https://arxiv.org/abs/2311.01906), [Tweet](https://x.com/maksym_andr/status/1722235666724192688?s=20)     |
| 3) **Understanding In-Context Learning Abilities in Transformers** - investigates how effectively transformers can bridge between pretraining data mixture to identify and learn new tasks in-context which are both inside and outside the pretraining distribution; in the regimes studied, there is limited evidence that the models’ in-context learning behavior is capable of generalizing beyond their pretraining data.                                            | [Paper](https://arxiv.org/abs/2311.00871), [Tweet](https://x.com/abacaj/status/1721223737729581437?s=20)          |
| 4) **MusicGen** - a single-stage transformer-based LLM that operates over several streams of compressed discrete music representation; it can generate high-quality samples                                                                                                                                                                                                                                                                                                | [Paper](https://arxiv.org/abs/2306.05284), [Tweet](https://x.com/AIatMeta/status/1723043913638810025?s=20)        |
| 5) **AltUp** - a method that makes it possible to take advantage of increasing scale and capacity in Transformer models without increasing the computational cost; achieved by working on a subblock of the widened representation at each layer and using a predict-and-correct mechanism to update the inactivated blocks; it widens the learn representation while only incurring a negligible increase in latency.                                                     | [Paper](https://arxiv.org/abs/2301.13310), [Tweet](https://x.com/GoogleAI/status/1722004366201418132?s=20)        |
| 6) **Rephrase and Respond** - an effective prompting method that uses LLMs to rephrase and expand questions posed by humans to improve overall performance; it can improve the performance of different models across a wide range of tasks; the approach can be combined with chain-of-thought to improve performance further.                                                                                                                                            | [Paper](https://arxiv.org/abs/2311.04205), [Tweet](https://x.com/QuanquanGu/status/1722364144379396513?s=20)      |
| 7) **On the Road with GPT-4V(ision)** - provides an exhaustive evaluation of the latest state-of-the-art visual language model, GPT-4V(vision), and its application in autonomous driving; the model demonstrates superior performance in scene understanding and causal reasoning compared to existing autonomous systems.                                                                                                                                                | [Paper](https://arxiv.org/abs/2311.05332), [Tweet](https://x.com/arankomatsuzaki/status/1722795897359139057?s=20) |
| 8) **GPT4All** - outlines technical details of the GPT4All model family along with the open-source repository that aims to democratize access to LLMs.                                                                                                                                                                                                                                                                                                                     | [Paper](https://arxiv.org/abs/2311.04931), [Tweet](https://x.com/_akhaliq/status/1722833378590793915?s=20)        |
| 9) **S-LoRA** - an approach that enables the scalable serving of many LoRA adapters; it stores all adapters in main memory and fetches adapters of currently running queries to the GPU memory; employs novel tensor parallelism strategy and highly optimized custom CUDA kernels for heterogenous batching of LoRA computation; improves throughput by 4x, when compared to other solutions, and increases the number of served adapters by several orders of magnitude. | [Paper](https://arxiv.org/abs/2311.03285v2), [Tweet](https://x.com/ai_database/status/1722190708797592013?s=20)   |
| 10) **FreshLLMs** - proposes a dynamic QA benchmark                                                                                                                                                                                                                                                                                                                                                                                                                        | [Paper](https://arxiv.org/abs/2310.03214), [Tweet](https://x.com/_akhaliq/status/1710108355157487635?s=20)        |

---

## Top ML Papers of the Week (October 30 - November 5)

| **Paper**                                                                                                                                                                                                                                                                                                                                                                                                                                    | **Links**                                                                                                                                                                                                                 |
| -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1) **MetNet-3** - a state-of-the-art neural weather model that extends both the lead time range and the variables that an observation-based model can predict well; learns from both dense and sparse data sensors and makes predictions up to 24 hours ahead for precipitation, wind, temperature, and dew point.                                                                                                                           | [Paper](https://arxiv.org/abs/2306.06079), [Tweet](https://x.com/GoogleAI/status/1719774923294687636?s=20)                                                                                                                |
| 2) **Evaluating LLMs** - a comprehensive survey                                                                                                                                                                                                                                                                                                                                                                                              | [Paper](https://arxiv.org/abs/2310.19736), [Tweet](https://x.com/omarsar0/status/1719351676828602502?s=20)                                                                                                                |
| 3) **Battle of the Backbones** - a large benchmarking framework for a diverse suite of computer vision tasks; find that while vision transformers                                                                                                                                                                                                                                                                                            | [Paper](https://arxiv.org/abs/2310.19909), [Tweet](https://x.com/micahgoldblum/status/1719719308882801045?s=20)                                                                                                           |
| 4) **LLMs for Chip Design** - proposes using LLMs for industrial chip design by leveraging domain adaptation techniques; evaluates different applications for chip design such as assistant chatbot, electronic design automation, and bug summarization; domain adaptation significantly improves performance over general-purpose models on a variety of design tasks; using a domain-adapted LLM for RAG further improves answer quality. | [Paper](https://arxiv.org/abs/2311.00176), [Tweet](https://x.com/omarsar0/status/1720066328961159387?s=20)                                                                                                                |
| 5) **Efficient Context Window Extension of LLMs** - proposes a compute-efficient method for efficiently extending the context window of LLMs beyond what it was pretrained on; extrapolates beyond the limited context of a fine-tuning dataset and models have been reproduced up to 128K context length.                                                                                                                                   | [Paper](https://arxiv.org/abs/2309.00071), [Tweet](https://x.com/theemozilla/status/1720107186850877662?s=20)                                                                                                             |
| 6) **Open DAC 2023** - introduces a dataset consisting of more than 38M density functional theory                                                                                                                                                                                                                                                                                                                                            | [Paper](https://arxiv.org/abs/2311.00341), [Tweet](https://x.com/AIatMeta/status/1720143486505341128?s=20)                                                                                                                |
| 7) **Symmetry in Machine Learning** - presents a unified and methodological framework to enforce, discover, and promote symmetry in machine learning; also discusses how these ideas can be applied to ML models such as multilayer perceptions and basis function regression.                                                                                                                                                               | [Paper](https://arxiv.org/abs/2311.00212), [Tweet](https://x.com/eigensteve/status/1720115655050227911?s=20)                                                                                                              |
| 8) **Next Generation AlphaFold** - reports progress on a new iteration of AlphaFold that greatly expands its range of applicability; shows capabilities of joint structure prediction of complexes including proteins, nucleic acids, small molecules, ions, and modified residue; demonstrates greater accuracy on protein-nucleic acid interactions than specialists predictors.                                                           | [Paper](https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/a-glimpse-of-the-next-generation-of-alphafold/alphafold_latest_oct2023.pdf), [Tweet](https://x.com/demishassabis/status/1719345831730368596?s=20) |
| 9) **Enhancing LLMs by Emotion Stimuli** - explores the ability of LLMs to understand emotional stimuli; conducts automatic experiments on 45 tasks using various LLMs, including Flan-T5-Large, Vicuna, Llama 2, BLOOM, ChatGPT, and GPT-4; the tasks span deterministic and generative applications that represent comprehensive evaluation scenarios; experimental results show that LLMs have a grasp of emotional intelligence.         | [Paper](https://arxiv.org/abs/2307.11760), [Tweet](https://x.com/emollick/status/1720135672764285176?s=20)                                                                                                                |
| 10) **FP8-LM** - finds that when training FP8 LLMs most variables, such as gradients and optimizer states, in LLM training, can employ low-precision data formats without compromising model accuracy and requiring no changes to hyper-parameter.                                                                                                                                                                                           | [Paper](https://arxiv.org/abs/2310.18313), [Tweet](https://x.com/arankomatsuzaki/status/1718813303223222765?s=20)                                                                                                         |

---

## Top ML Papers of the Week (October 23 - October 29)

| **Paper**                                                                                                                                                                                                                                                                                                                                                                                     | **Links**                                                                                                                           |
| --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- |
| 1) **Zephyr LLM** - a 7B parameter model with competitive performance to ChatGPT on AlpacaEval; applies distilled supervised fine-tuning to improve task accuracy and distilled direct performance optimization on AI feedback data to better align the model; shows performance comparable to 70B-parameter chat models aligned with human feedback.                                         | [Paper](https://arxiv.org/abs/2310.16944), [Tweet](https://x.com/nazneenrajani/status/1717747969842417723?s=20)                     |
| 2) **Fact-checking with LLMs** - investigates the fact-checking capabilities of LLMs like GPT-4; results show the enhanced prowess of LLMs when equipped with contextual information; GPT4 outperforms GPT-3, but accuracy varies based on query language and claim veracity; while LLMs show promise in fact-checking, they demonstrate inconsistent accuracy.                               | [Paper](https://arxiv.org/abs/2310.13549), [Tweet](https://x.com/omarsar0/status/1717550929145119212?s=20)                          |
| 3) **Matryoshka Diffusion Models** - introduces an end-to-end framework for high-resolution image and video synthesis; involves a diffusion process that denoises inputs at multiple resolutions jointly and uses a NestedUNet architecture; enables a progressive training schedule from lower to higher resolutions leading to improvements in optimization for high-resolution generation. | [Paper](https://arxiv.org/abs/2310.15111), [Tweet](https://x.com/thoma_gu/status/1716923384846856691?s=20)                          |
| 4) **Spectron** - a new approach for spoken language modeling trained end-to-end to directly process spectrograms; it can be fine-tuned to generate high-quality accurate spoken language; the method surpasses existing spoken language models in speaker preservation and semantic coherence.                                                                                               | [Paper](https://arxiv.org/abs/2305.15255), [Tweet](https://x.com/GoogleAI/status/1717584836834001066?s=20)                          |
| 5) **LLMs Meet New Knowledge** - presents a benchmark to assess LLMs' abilities in knowledge understanding, differentiation, and association; benchmark results show                                                                                                                                                                                                                          | [Paper](https://arxiv.org/abs/2310.14820), [Tweet](https://x.com/omarsar0/status/1716817266195796186?s=20)                          |
| 6) **Detecting Pretraining Data from LLMs** - explores the problem of pretraining data detection which aims to determine if a black box model was trained on a given text; proposes a detection method named Min-K% Prob as an effective tool for benchmark example contamination detection, privacy auditing of machine unlearning, and copyrighted text detection in LM’s pertaining data.  | [Paper](https://arxiv.org/abs/2310.16789), [Tweet](https://x.com/WeijiaShi2/status/1717612387174687150?s=20)                        |
| 7) **ConvNets Match Vision Transformers** - evaluates a performant ConvNet architecture pretrained on JFT-4B at scale; observes a log-log scaling law between the held out loss and compute budget; after fine-tuning on ImageNet, NFNets match the reported performance of Vision Transformers with comparable compute budgets.                                                              | [Paper](https://arxiv.org/abs/2310.16764), [Tweet](https://x.com/_akhaliq/status/1717385905214759421?s=20)                          |
| 8) **CommonCanvas** - a dataset of Creative-Commons-licensed                                                                                                                                                                                                                                                                                                                                  | [Paper](https://arxiv.org/abs/2310.16825), [Tweet](https://x.com/iScienceLuvr/status/1717359916422496596?s=20)                      |
| 9) **Managing AI Risks** - a short paper outlining risks from upcoming and advanced AI systems, including an examination of social harms, malicious uses, and other potential societal issues emerging from the rapid adoption of autonomous AI systems.                                                                                                                                      | [Paper](https://managing-ai-risks.com/managing_ai_risks.pdf), [Tweet](https://x.com/geoffreyhinton/status/1717967329202491707?s=20) |
| 10) **Branch-Solve-Merge Reasoning in LLMs** - an LLM program that consists of branch, solve, and merge modules parameterized with specific prompts to the base LLM; this enables an LLM to plan a decomposition of task into multiple parallel sub-tasks, independently solve them, and fuse solutions to the sub-tasks; improves evaluation correctness and consistency for multiple LLMs.  | [Paper](https://arxiv.org/abs/2310.15123), [Tweet](https://x.com/jaseweston/status/1716635331393380619?s=20)                        |

---

## Top ML Papers of the Week (October 16 - October 22)

| **Paper**                                                                                                                                                                                                                                                                                                                                                                                                 | **Links**                                                                                                                                                                                                        |
| --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1) **Llemma** - an LLM for mathematics which is based on continued pretraining from Code Llama on the Proof-Pile-2 dataset; the dataset involves scientific paper, web data containing mathematics, and mathematical code; Llemma outperforms open base models and the unreleased Minerva on the MATH benchmark; the model is released, including dataset and code to replicate experiments.              | [Paper](https://arxiv.org/abs/2310.10631), [Tweet](https://x.com/zhangir_azerbay/status/1714098025956864031?s=20)                                                                                                |
| 2) **LLMs for Software Engineering** - a comprehensive survey of LLMs for software engineering, including open research and technical challenges.                                                                                                                                                                                                                                                         | [Paper](https://arxiv.org/abs/2310.03533), [Tweet](https://x.com/omarsar0/status/1713940983199506910?s=20)                                                                                                       |
| 3) **Self-RAG** - presents a new retrieval-augmented framework that enhances an LM’s quality and factuality through retrieval and self-reflection; trains an LM that adaptively retrieves passages on demand, and generates and reflects on the passages and its own generations using special reflection tokens; it significantly outperforms SoTA LLMs                                                  | [Paper](https://arxiv.org/abs/2310.11511), [Tweet](https://x.com/AkariAsai/status/1715110277077962937?s=20)                                                                                                      |
| 4) **Retrieval-Augmentation for Long-form Question Answering** - explores retrieval-augmented language models on long-form question answering; finds that retrieval is an important component but evidence documents should be carefully added to the LLM; finds that attribution error happens more frequently when retrieved documents lack sufficient information/evidence for answering the question. | [Paper](https://arxiv.org/abs/2310.12150), [Tweet](https://x.com/omarsar0/status/1714986431859282144?s=20)                                                                                                       |
| 5) **GenBench** - presents a framework for characterizing and understanding generalization research in NLP; involves a meta-analysis of 543 papers and a set of tools to explore and better understand generalization studies.                                                                                                                                                                            | [Paper](https://www.nature.com/articles/s42256-023-00729-y?utm_source=twitter&utm_medium=organic_social&utm_campaign=research&utm_content=link), [Tweet](https://x.com/AIatMeta/status/1715041427283902793?s=20) |
| 6) **A Study of LLM-Generated Self-Explanations** - assesses an LLM's capability to self-generate feature attribution explanations; self-explanation is useful to improve performance and truthfulness in LLMs; this capability can be used together with chain-of-thought prompting.                                                                                                                     | [Paper](https://arxiv.org/abs/2310.11207), [Tweet](https://x.com/omarsar0/status/1714665747752923620?s=20)                                                                                                       |
| 7) **OpenAgents** - an open platform for using and hosting language agents in the wild; includes three agents, including a Data Agent for data analysis, a Plugins Agent with 200+ daily API tools, and a Web Agent for autonomous web browsing.                                                                                                                                                          | [Paper](https://arxiv.org/abs/2310.10634v1), [Tweet](https://x.com/ChengZhoujun/status/1714343204148113860?s=20)                                                                                                 |
| 8) **Eliciting Human Preferences with LLMs** - uses language models to guide the task specification process and a learning framework to help models elicit and infer intended behavior through free-form, language-based interaction with users; shows that by generating open-ended questions, the system generates responses that are more informative than user-written prompts.                       | [Paper](https://arxiv.org/abs/2310.11589), [Tweet](https://x.com/AlexTamkin/status/1715040019520569395?s=20)                                                                                                     |
| 9) **AutoMix** - an approach to route queries to LLMs based on the correctness of smaller language models                                                                                                                                                                                                                                                                                                 | [Paper](https://arxiv.org/abs/2310.12963), [Tweet](https://x.com/omarsar0/status/1715385477627334718?s=20)                                                                                                       |
| 10) **Video Language Planning** - enables synthesizing complex long-horizon video plans across robotics domains; the proposed algorithm involves a tree search procedure that trains vision-language models to serve as policies and value functions, and text-to-video models as dynamic models.                                                                                                         | [Paper](https://arxiv.org/abs/2310.10625), [Tweet](https://x.com/du_yilun/status/1714297584842318157?s=20)                                                                                                       |

---

## Top ML Papers of the Week (October 9 - October 15)

| **Paper**                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | **Links**                                                                                                       |
|   | --------------------------------------------------------------------------------------------------------------- |
| 1) **Ring Attention** - a memory-efficient approach that leverages blockwise computation of self-attention to distribute long sequences across multiple devices to overcome the memory limitations inherent in Transformer architectures, enabling handling of longer sequences during training and inference; enables scaling the context length with the number of devices while maintaining performance, exceeding context length of 100 million without attention approximations.                                                                                                                                                                                            | [Paper](https://arxiv.org/abs/2310.01889), [Tweet](https://x.com/haoliuhl/status/1709630382457733596?s=20)      |
| 2) **Universal Simulator** - applies generative modeling to learn a universal simulator of real-world interactions; can emulate how humans and agents interact with the world by simulating the visual outcome of high instruction and low-level controls; the system can be used to train vision-language planners, low-level reinforcement learning policies, and even for systems that perform video captioning.                                                                                                                                                                                                                                                              | [Paper](https://arxiv.org/abs/2310.06114), [Tweet](https://x.com/mengjiao_yang/status/1712153304757915925?s=20) |
| 3) **Overview of Factuality in LLMs** - a survey of factuality in LLMs providing insights into how to evaluate factuality in LLMs and how to enhance it.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         | [Paper](https://arxiv.org/abs/2310.07521), [Tweet](https://x.com/omarsar0/status/1712469661118517740?s=20)      |
| 4) **LLMs can Learn Rules** - presents a two-stage framework that learns a rule library for reasoning with LLMs; in the first stage                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | [Paper](https://arxiv.org/abs/2310.07064), [Tweet](https://x.com/zhu_zhaocheng/status/1712582734550647091?s=20) |
| 5) **Meta Chain-of-Thought Prompting** - a generalizable chain-of-thought                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | [Paper](https://arxiv.org/abs/2310.06692), [Tweet](https://x.com/omarsar0/status/1712835499256090972?s=20)      |
| 6) **A Survey of LLMs for Healthcare** - a comprehensive overview of LLMs applied to the healthcare domain.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | [Paper](https://arxiv.org/abs/2310.05694), [Tweet](https://x.com/omarsar0/status/1711755055777415485?s=20)      |
| 7) **Improving Retrieval-Augmented LMs with Compressors** - presents two approaches to compress retrieved documents into text summaries before pre-pending them in-context: 1) extractive compressor - selects useful sentences from retrieved documents 2) abstractive compressor - generates summaries by synthesizing information from multiple documents; achieves a compression rate of as low as 6% with minimal loss in performance on language modeling tasks and open domain question answering tasks; the proposed training scheme performs selective augmentation which helps to generate empty summaries when retrieved docs are irrelevant or unhelpful for a task. | [Paper](https://arxiv.org/abs/2310.04408), [Tweet](https://x.com/omarsar0/status/1711384213092479130?s=20)      |
| 8) **Instruct-Retro** - introduces Retro 48B, the largest LLM pretrained with retrieval; continues pretraining a 43B parameter GPT model on an additional 100B tokens by retrieving from 1.2T tokens                                                                                                                                                                                                                                                                                                                                                                                                                                                                             | [Paper](https://arxiv.org/abs/2310.07713), [Tweet](https://x.com/omarsar0/status/1712466049428521433?s=20)      |
| 9) **MemWalker** - a method to enhance long-text understanding by treating the LLM as an interactive agent that can decide how to read the text via iterative prompting; it first processes long context into a tree of summer nodes and reads in a query to traverse the tree, seeking relevant information and crafting a suitable response; this process is achieved through reasoning and enables effective reading and enhances explainability through reasoning steps.                                                                                                                                                                                                     | [Paper](https://arxiv.org/abs/2310.05029), [Tweet](https://x.com/__howardchen/status/1711584916708938042?s=20)  |
| 10) **Toward Language Agent Fine-tuning** - explores the direction of fine-tuning LLMs to obtain language agents; finds that language agents consistently improved after fine-tuning their backbone language model; claims that fine-tuning a Llama2-7B with 500 agent trajectories                                                                                                                                                                                                                                                                                                                                                                                              | [Paper](https://arxiv.org/abs/2310.05915), [Tweet](https://x.com/omarsar0/status/1711757242905534479?s=20)      |

---

## Top ML Papers of the Week (October 2 - October 8)

| **Paper**                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | **Links**                                                                                                        |
|   | ---------------------------------------------------------------------------------------------------------------- |
| 1) **LLMs Represent Space and Time** - discovers that LLMs learn linear representations of space and time across multiple scales; the representations are robust to prompt variations and unified across different entity types; demonstrate that LLMs acquire fundamental structured knowledge such as space and time, claiming that language models learn beyond superficial statistics, but literal world models.                                                                                                                                                                                                                                                                                                      | [Paper](https://arxiv.org/abs/2310.02207), [Tweet](https://x.com/wesg52/status/1709551516577902782?s=20)         |
| 2) **Retrieval meets Long Context LLMs** - compares retrieval augmentation and long-context windows for downstream tasks to investigate if the methods can be combined to get the best of both worlds; an LLM with a 4K context window using simple RAG can achieve comparable performance to a fine-tuned LLM with 16K context; retrieval can significantly improve the performance of LLMs regardless of their extended context window sizes; a retrieval-augmented LLaMA2-70B with a 32K context window outperforms GPT-3.5-turbo-16k on seven long context tasks including question answering and query-based summarization.                                                                                          | [Paper](https://arxiv.org/abs/2310.03025), [Tweet](https://x.com/omarsar0/status/1709749178199318545?s=20)       |
| 3) **StreamingLLM** - a framework that enables efficient streaming LLMs with attention sinks, a phenomenon where the KV states of initial tokens will largely recover the performance of window attention; the emergence of the attention sink is due to strong attention scores towards the initial tokens; this approach enables LLMs trained with finite length attention windows to generalize to infinite sequence length without any additional fine-tuning.                                                                                                                                                                                                                                                        | [Paper](https://arxiv.org/abs/2309.17453), [Tweet](https://x.com/Guangxuan_Xiao/status/1708943505731801325?s=20) |
| 4) **Neural Developmental Programs** - proposes to use neural networks that self-assemble through a developmental process that mirrors properties of embryonic development in biological organisms                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        | [Paper](https://arxiv.org/abs/2307.08197), [Tweet](https://x.com/risi1979/status/1708888992224362742?s=20)       |
| 5) **The Dawn of LMMs** - a comprehensive analysis of GPT-4V to deepen the understanding of large multimodal models                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | [Paper](https://arxiv.org/abs/2309.17421), [Tweet](https://x.com/omarsar0/status/1708860551110041871?s=20)       |
| 6) **Training LLMs with Pause Tokens** - performs training and inference on LLMs with a learnable <pause> token which helps to delay the model's answer generation and attain performance gains on general understanding tasks of Commonsense QA and math word problem-solving; experiments show that this is only beneficial provided that the delay is introduced in both pertaining and downstream fine-tuning.                                                                                                                                                                                                                                                                                                        | [Paper](https://arxiv.org/abs/2310.02226), [Tweet](https://x.com/omarsar0/status/1709573238123122959?s=20)       |
| 7) **Recursively Self-Improving Code Generation** - proposes the use of a language model-infused scaffolding program to recursively improve itself; a seed improver first improves an input program that returns the best solution which is then further tasked to improve itself; shows that the GPT-4 models can write code that can call itself to improve itself.                                                                                                                                                                                                                                                                                                                                                     | [Paper](https://arxiv.org/abs/2310.02304), [Tweet](https://x.com/ericzelikman/status/1709721771937587541?s=20)   |
| 8) **Retrieval-Augmented Dual Instruction Tuning** - proposes a lightweight fine-tuning method to retrofit LLMs with retrieval capabilities; it involves a 2-step approach: 1) updates a pretrained LM to better use the retrieved information 2) updates the retriever to return more relevant results, as preferred by the LM Results show that fine-tuning over tasks that require both knowledge utilization and contextual awareness, each stage leads to additional gains; a 65B model achieves state-of-the-art results on a range of knowledge-intensive zero- and few-shot learning benchmarks; it outperforms existing retrieval-augmented language approaches by up to +8.9% in zero-shot and +1.4% in 5-shot. | [Paper](https://arxiv.org/abs/2310.01352), [Tweet](https://x.com/omarsar0/status/1709204756013490494?s=20)       |
| 9) **KOSMOG-G** - a model that performs high-fidelity zero-shot image generation from generalized vision-language input that spans multiple images; extends zero-shot subject-driven image generation to multi-entity scenarios; allows the replacement of CLIP, unlocking new applications with other U-Net techniques such as ControlNet and LoRA.                                                                                                                                                                                                                                                                                                                                                                      | [Paper](https://arxiv.org/abs/2310.02992), [Tweet](https://x.com/omarsar0/status/1709934741158510625?s=20)       |
| 10) **Analogical Prompting** - a new prompting approach to automatically guide the reasoning process of LLMs; the approach is different from chain-of-thought in that it doesn’t require labeled exemplars of the reasoning process; the approach is inspired by analogical reasoning and prompts LMs to self-generate relevant exemplars or knowledge in the context.                                                                                                                                                                                                                                                                                                                                                    | [Paper](https://arxiv.org/abs/2310.01714), [Tweet](https://x.com/michiyasunaga/status/1709582150025240854?s=20)  |

---

## Top ML Papers of the Week (September 25 - October 1)

| **Paper**                                                                                                                                                                                                                                                                                                                                                                                                                | **Links**                                                                                                                      |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------ |
| 1) **The Reversal Curse** - finds that LLMs trained on sentences of the form “A is B” will not automatically generalize to the reverse direction “B is A”, i.e., the Reversal Curse; shows the effect through finetuning LLMs on fictitious statements and demonstrating its robustness across model sizes and model families.                                                                                           | [Paper](https://owainevans.github.io/reversal_curse.pdf), [Tweet](https://x.com/OwainEvans_UK/status/1705285631520407821?s=20) |
| 2) **Effective Long-Context Scaling with LLMs** - propose a 70B variant that can already surpass gpt-3.5-turbo-16k’s overall performance on a suite of long-context tasks. This involves a cost-effective instruction tuning procedure that does not require human-annotated long instruction data.                                                                                                                      | [Paper](https://arxiv.org/abs/2309.16039), [Tweet](https://x.com/omarsar0/status/1707780482178400261?s=20)                     |
| 3) **Graph Neural Prompting with LLMs** - proposes a plug-and-play method to assist pre-trained LLMs in learning beneficial knowledge from knowledge graphs                                                                                                                                                                                                                                                              | [Paper](https://arxiv.org/abs/2309.15427), [Tweet](https://x.com/omarsar0/status/1707211751354212382?s=20)                     |
| 4) **Vision Transformers Need Registers** - identifies artifacts in feature maps of vision transformer networks that are repurposed for internal computations; this work proposes a solution to provide additional tokens to the input sequence to fill that role; the solution fixes the problem, leads to smoother feature and attention maps, and sets new state-of-the-art results on dense visual prediction tasks. | [Paper](https://arxiv.org/abs/2309.16588), [Tweet](https://x.com/TimDarcet/status/1707769575981424866?s=20)                    |
| 5) **Boolformer** - presents the first Transformer architecture trained to perform end-to-end symbolic regression of Boolean functions; it can predict compact formulas for complex functions and be applied to modeling the dynamics of gene regulatory networks.                                                                                                                                                       | [Paper](https://arxiv.org/abs/2309.12207), [Tweet](https://x.com/stephanedascoli/status/1706235856778834015?s=20)              |
| 6) **LlaVA-RLHF** - adapts factually augmented RLHF to aligning large multimodal models; this approach alleviates the reward hacking in RLHF and improves performance on the LlaVA-Bench dataset with the 94% performance level of the text-only GPT-4.                                                                                                                                                                  | [Paper](https://arxiv.org/abs/2309.14525), [Tweet](https://x.com/arankomatsuzaki/status/1706839311306621182?s=20)              |
| 7) **LLM Alignment Survey** - a comprehensive survey paper on LLM alignment; topics include Outer Alignment, Inner Alignment, Mechanistic Interpretability, Attacks on Aligned LLMs, Alignment Evaluation, Future Directions, and Discussions.                                                                                                                                                                           | [Paper](https://arxiv.org/abs/2309.15025), [Tweet](https://x.com/omarsar0/status/1706845285064818905?s=20)                     |
| 8) **Qwen LLM** - proposes a series of LLMs demonstrating the strength of RLHF on tasks involving tool use and planning capabilities for creating language agents.                                                                                                                                                                                                                                                       | [Paper](https://arxiv.org/abs/2309.16609), [Tweet](https://x.com/omarsar0/status/1707776749042364729?s=20)                     |
| 9) **MentalLlaMa** - an open-source LLM series for interpretable mental health analysis with instruction-following capability; it also proposes a multi-task and multi-source interpretable mental health instruction dataset on social media with 105K data samples.                                                                                                                                                    | [Paper](https://arxiv.org/abs/2309.13567), [Tweet](https://x.com/SAnaniadou/status/1707668936634794442?s=20)                   |
| 10) **Logical Chain-of-Thought in LLMs** - a new neurosymbolic framework to improve zero-shot chain-of-thought reasoning in LLMs; leverages principles from symbolic logic to verify and revise reasoning processes to improve the reasoning capabilities of LLMs.                                                                                                                                                       | [Paper](https://arxiv.org/abs/2309.13339), [Tweet](https://x.com/omarsar0/status/1706711389803287019?s=20)                     |

---

## Top ML Papers of the Week (September 18 - September 24)

| **Paper**                                                                                                                                                                                                                                                                                                                                                                                                                             | **Links**                                                                                                                           |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- |
| 1) **AlphaMissense** - an AI model classifying missense variants to help pinpoint the cause of diseases; the model is used to develop a catalogue of genetic mutations; it can categorize 89% of all 71 million possible missense variants as either likely pathogenic or likely benign.                                                                                                                                              | [Paper](https://www.science.org/doi/10.1126/science.adg7492), [Tweet](https://x.com/GoogleDeepMind/status/1704145467129389178?s=20) |
| 2) **Chain-of-Verification reduces Hallucination in LLMs** - develops a method to enable LLMs to "deliberate" on responses to correct mistakes; include the following steps: 1) draft initial response, 2) plan verification questions to fact-check the draft, 3) answer questions independently to avoid bias from other responses, and 4) generate a final verified response.                                                      | [Paper](https://arxiv.org/abs/2309.11495), [Tweet](https://x.com/omarsar0/status/1704901425824772275?s=20)                          |
| 3) **Contrastive Decoding Improves Reasoning in Large Language Models** - shows that contrastive decoding leads Llama-65B to outperform Llama 2 and other models on commonsense reasoning and reasoning benchmarks.                                                                                                                                                                                                                   | [Paper](https://arxiv.org/abs/2309.09117), [Tweet](https://x.com/_akhaliq/status/1703966776990597567?s=20)                          |
| 4) **LongLoRA** - an efficient fine-tuning approach to significantly extend the context windows of pre-trained LLMs; implements shift short attention, a substitute that approximates the standard self-attention pattern during training; it has less GPU memory cost and training time compared to full fine-tuning while not compromising accuracy.                                                                                | [Paper](https://arxiv.org/abs/2309.12307), [Tweet](https://x.com/omarsar0/status/1705234482930798813?s=20)                          |
| 5) **LLMs for Generating Structured Data** - studies the use of LLMs for generating complex structured data; proposes a structure-aware fine-tuning method, applied to Llama-7B, which significantly outperform other model like GPT-3.5/4 and Vicuna-13B.                                                                                                                                                                            | [Paper](https://arxiv.org/abs/2309.08963), [Tweet](https://x.com/omarsar0/status/1703958549917847884?s=20)                          |
| 6) **LMSYS-Chat-1M** - a large-scale dataset containing 1 million real-world conversations with 25 state-of-the-art LLM; it is collected from 210K unique IP addresses on the Vincuna demo and Chatbot Arena website.                                                                                                                                                                                                                 | [Paper](http://arxiv.org/abs/2309.11998), [Tweet](https://x.com/arankomatsuzaki/status/1705024956122161217?s=20)                    |
| 7) **Language Modeling is Compression** - evaluates the compression capabilities of LLMs; it investigates how and why compression and prediction are equivalent; shows that LLMs are powerful general-purpose compressors due to their in-context learning abilities; finds that Chinchilla 70B compresses ImageNet patches to 43.4% and LibriSpeech samples to 16.4% of their raw size, beating domain-specific compressors like PNG | [Paper](https://arxiv.org/abs/2309.10668), [Tweet](https://x.com/omarsar0/status/1704306357006897402?s=20)                          |
| 8) **Compositional Foundation Models** - proposes foundation models that leverage multiple expert foundation models trained on language, vision, and action data to solve long-horizon goals.                                                                                                                                                                                                                                         | [Paper](https://arxiv.org/abs/2309.08587), [Tweet](https://x.com/du_yilun/status/1703786005612929214?s=20)                          |
| 9) **LLMs for IT Operations** - proposes OWL, an LLM for IT operations tuned using a self-instruct strategy based on IT-related tasks; it discusses how to collect a quality instruction dataset and how to put together a benchmark.                                                                                                                                                                                                 | [Paper](https://arxiv.org/abs/2309.09298), [Tweet](https://x.com/omarsar0/status/1704137910834888743?s=20)                          |
| 10) **KOSMOS-2.5** - a multimodal model for machine reading of text-intensive images, capable of document-level text generation and image-to-markdown text generation.                                                                                                                                                                                                                                                                | [Paper](https://arxiv.org/abs/2309.11419), [Tweet](https://x.com/arankomatsuzaki/status/1704659787399487649?s=20)                   |

---

## Top ML Papers of the Week (September 11 - September 17)

| **Paper**                                                                                                                                                                                                                                                                                                                                | **Links**                                                                                                                                   |
| ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------- |
| 1) **Textbooks Are All You Need II** - a new 1.3 billion parameter model trained on 30 billion tokens; the dataset consists of "textbook-quality" synthetically generated data; phi-1.5 competes or outperforms other larger models on reasoning tasks suggesting that data quality plays a more important role than previously thought. | [Paper](https://arxiv.org/abs/2309.05463), [Tweet](https://x.com/omarsar0/status/1701590130270601422?s=20)                                  |
| 2) **The Rise and Potential of LLM Based Agents** - a comprehensive overview of LLM based agents; covers from how to construct these agents to how to harness them for good.                                                                                                                                                             | [Paper](https://arxiv.org/abs/2309.07864), [Tweet](https://x.com/omarsar0/status/1702736490067890239?s=20)                                  |
| 3) **EvoDiff** - combines evolutionary-scale data with diffusion models for controllable protein generation in sequence space; it can generate proteins inaccessible to structure-based models.                                                                                                                                          | [Paper](https://www.biorxiv.org/content/10.1101/2023.09.11.556673v1), [Tweet](https://x.com/KevinKaichuang/status/1701953715312136302?s=20) |
| 4) **LLMs Can Align Themselves without Finetuning?** - discovers that by integrating self-evaluation and rewind mechanisms, unaligned LLMs can directly produce responses consistent with human preferences via self-boosting.                                                                                                           | [Paper](https://arxiv.org/abs/2309.07124), [Tweet](https://x.com/omarsar0/status/1702131444041011395?s=20)                                  |
| 5) **Robot Parkour Learning** - presents a system for learning end-to-end vision-based parkour policy which is transferred to a quadrupedal robot using its ecocentric depth camera; shows that low-cost robots can automatically select and execute parkour skills in a real-world environment.                                         | [Paper](https://arxiv.org/abs/2309.05665), [Tweet](https://x.com/zipengfu/status/1701316023612219445?s=20)                                  |
| 6) **A Survey of Hallucination in LLMs** - classifies different types of hallucination phenomena and provides evaluation criteria for assessing hallucination along with mitigation strategies.                                                                                                                                          | [Paper](https://arxiv.org/abs/2309.05922), [Tweet](https://x.com/omarsar0/status/1701970034711539839?s=20)                                  |
| 7) **Agents** - an open-source library for building autonomous language agents including support for features like planning, memory, tool usage, multi-agent communication, and more.                                                                                                                                                    | [Paper](https://arxiv.org/abs/2309.07870), [Tweet](https://x.com/arankomatsuzaki/status/1702497897395396960?s=20)                           |
| 8) **Radiology-Llama2: Best-in-Class LLM for Radiology** - presents an LLM based on Llama 2 tailored for radiology; it's tuned on a large dataset of radiology reports to generate coherent and clinically useful impressions from radiology findings.                                                                                   | [Paper](https://arxiv.org/abs/2309.06419), [Tweet](https://x.com/omarsar0/status/1701774444052557965?s=20)                                  |
| 9) **Communicative Agents for Software Development** - presents ChatDev, a virtual chat-powered software development company mirroring the waterfall model; shows the efficacy of the agent in software generation, even completing the entire software development process in less than seven minutes for less than one dollar.         | [Paper](https://arxiv.org/abs/2307.07924v3), [Tweet](https://x.com/KevinAFischer/status/1702355125418045860?s=20)                           |
| 10) **MAmmoTH** - a series of open-source LLMs tailored for general math problem-solving; the models are trained on a curated instruction tuning dataset and outperform existing open-source models on several mathematical reasoning datasets.                                                                                          | [Paper](https://arxiv.org/abs/2309.05653), [Tweet](https://x.com/xiangyue96/status/1701710215442309323?s=20)                                |

---

## Top ML Papers of the Week (September 4 - September 10)

| **Paper**                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            | **Links**                                                                                                               |
| ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
| 1) **Transformers as SVMs** - finds that the optimization geometry of self-attention in Transformers exhibits a connection to hard-margin SVM problems; also finds that gradient descent applied without early-stopping leads to implicit regularization and convergence of self-attention; this work has the potential to deepen the understanding of language models.                                                                                                                              | [Paper](https://arxiv.org/abs/2308.16898)                                                                               |
| 2) **Scaling RLHF with AI Feedback** - tests whether RLAIF is a suitable alternative to RLHF by comparing the efficacy of human vs. AI feedback; uses different techniques to generate AI labels and conduct scaling studies to report optimal settings for generating aligned preferences; the main finding is that on the task of summarization, human evaluators prefer generations from both RLAIF and RLHF over a baseline SFT model in ∼70% of cases.                                          | [Paper](https://arxiv.org/abs/2309.00267), [Tweet](https://twitter.com/omarsar0/status/1699102486928265530?s=20)        |
| 3) **GPT Solves Math Problems Without a Calculator** - shows that with sufficient training data, a 2B language model can perform multi-digit arithmetic operations with 100% accuracy and without data leakage; it’s also competitive with GPT-4 on 5K samples Chinese math problem test set when fine-tuned from GLM-10B on a dataset containing additional multi-step arithmetic operations and detailed math problems.                                                                            | [Paper](https://arxiv.org/abs/2309.03241), [Tweet](https://twitter.com/_akhaliq/status/1699951105927512399?s=20)        |
| 4) **LLMs as Optimizers** - an approach where the optimization problem is described in natural language; an LLM is then instructed to iteratively generate new solutions based on the defined problem and previously found solutions; at each optimization step, the goal is to generate new prompts that increase test accuracy based on the trajectory of previously generated prompts; the optimized prompts outperform human-designed prompts on GSM8K and Big-Bench Hard, sometimes by over 50% | [Paper](https://arxiv.org/abs/2309.03409), [Tweet](https://twitter.com/omarsar0/status/1700249035456598391?s=20)        |
| 5) **Multi-modality Instruction Tuning** - presents ImageBind-LLM, a multimodality instruction tuning method of LLMs via ImageBind; this model can respond to instructions of diverse modalities such as audio, 3D point clouds, and video, including high language generation quality; this is achieved by aligning ImageBind’s visual encoder with an LLM via learnable bind network.                                                                                                              | [Paper](https://arxiv.org/abs/2309.03905), [Tweet](https://twitter.com/arankomatsuzaki/status/1699947731333345750?s=20) |
| 6) **Explaining Grokking** - aims to explain grokking behavior in neural networks; specifically, it predicts and shows two novel behaviors: the first is ungrokking where a model goes from perfect generalization to memorization when trained further on a smaller dataset than the critical threshold; the second is semi-grokking where a network demonstrates grokking-like transition when training a randomly initialized network on the critical dataset size.                               | [Paper](https://arxiv.org/abs/2309.02390), [Tweet](https://twitter.com/VikrantVarma_/status/1699823229307699305?s=20)   |
| 7) **Overview of AI Deception** - provides a survey of empirical examples of AI deception.                                                                                                                                                                                                                                                                                                                                                                                                           | [Paper](https://arxiv.org/abs/2308.14752), [Tweet](https://twitter.com/DanHendrycks/status/1699437800301752332?s=20)    |
| 8) **FLM-101B** - a new open LLM called FLM-101B with 101B parameters and 0.31TB tokens which can be trained on a $100K budget; the authors analyze different growth strategies, growing the number of parameters from smaller sizes to large ones. They ultimately employ an aggressive strategy that reduces costs by >50%. In other words, three models are trained sequentially with each model inheriting knowledge from its smaller predecessor                                                | [Paper](https://arxiv.org/abs/2309.03852), [Tweet](https://twitter.com/omarsar0/status/1700156132700963053?s=20)        |
| 9) **Cognitive Architecture for Language Agents** - proposes a systematic framework for understanding and building fully-fledged language agents drawing parallels from production systems and cognitive architectures; it systematizes diverse methods for LLM-based reasoning, grounding, learning, and decision making as instantiations of language agents in the framework.                                                                                                                     | [Paper](https://arxiv.org/abs/2309.02427), [Tweet](https://twitter.com/ShunyuYao12/status/1699396834983362690?s=20)     |
| 10) **Q-Transformer** - a scalable RL method for training multi-task policies from large offline datasets leveraging human demonstrations and autonomously collected data; shows good performance on a large diverse real-world robotic manipulation task suite.                                                                                                                                                                                                                                     | [Paper](https://q-transformer.github.io/), [Tweet](https://twitter.com/YevgenChebotar/status/1699909244743815677?s=20)  |

---

## Top ML Papers of the Week (August 28 - September 3)

| **Paper**                                                                                                                                                                                                                                                                                                                                                                                                         | **Links**                                                                                                               |
| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
| 1) **Large Language and Speech Model** - proposes a large language and speech model trained with cross-modal conversational abilities that supports speech-and-language instruction enabling more natural interactions with AI systems.                                                                                                                                                                           | [Paper](https://arxiv.org/abs/2308.15930v1), [Tweet](https://twitter.com/_akhaliq/status/1697081112164475304?s=20)      |
| 2) **SAM-Med2D** - applies segment anything models                                                                                                                                                                                                                                                                                                                                                                | [Paper](https://arxiv.org/abs/2308.16184v1), [Tweet](https://twitter.com/omarsar0/status/1698014448856773102?s=20)      |
| 3) **Vector Search with OpenAI Embeddings** - suggests that “from a cost–benefit analysis, there does not appear to be a compelling reason to introduce a dedicated vector store into a modern “AI stack” for search since such applications have already received substantial investments in existing, widely deployed infrastructure.”                                                                          | [Paper](https://arxiv.org/abs/2308.14963), [Tweet](https://twitter.com/omarsar0/status/1696879909950361867?s=20)        |
| 4) **Graph of Thoughts** - presents a prompting approach that models text generated by LLMs as an arbitrary graph; it enables combining arbitrary "thoughts" and enhancing them using feedback loops; the core idea is to enhance the LLM capabilities through "network reasoning" and without any model updates; this could be seen as a generalization of the now popular Chain-of-Thought and Tree-of-Thought. | [Paper](https://arxiv.org/abs/2308.09687v2), [Tweet](https://twitter.com/omarsar0/status/1697245998828204200?s=20)      |
| 5) **MVDream** - a multi-view diffusion model that can generate geometrically consistent multi-view images given a text prompt; it leverages pre-trained diffusion models and a multi-view dataset rendered from 3D assets; this leads to generalizability of 2D diffusion and consistency of 3D data.                                                                                                            | [Paper](https://arxiv.org/abs/2308.16512), [Tweet](https://twitter.com/_akhaliq/status/1697521847963619462?s=20)        |
| 6) **Nougat** - proposes an approach for neural optical understanding of academic documents; it supports the ability to extract text, equations, and tables from academic PDFs, i.e., convert PDFs into LaTeX/markdown.                                                                                                                                                                                           | [Paper](https://arxiv.org/abs/2308.13418v1), [Tweet](https://twitter.com/lukas_blecher/status/1696101110853910716?s=20) |
| 7) **Factuality Detection in LLMs** - proposes a tool called **FacTool** to detect factual errors in texts generated by LLMs; shows the necessary components needed and the types of tools to integrate with LLMs for better detecting factual errors.                                                                                                                                                            | [Paper](https://arxiv.org/abs/2307.13528v2), [Tweet](https://twitter.com/omarsar0/status/1697642048587694370?s=20)      |
| 8) **AnomalyGPT** - an approach for industrial anomaly detection based on large vision-language models; it simulates anomalous images and textual descriptions to generate training data; employs an image decoder and prompt learner to detect anomalies; it shows few-shot in-context learning capabilities and achieves state-of-the-art performance benchmark datasets.                                       | [Paper](https://arxiv.org/abs/2308.15366v1), [Tweet](https://twitter.com/shinmura0/status/1697091364633317707?s=20)     |
| 9) **FaceChain** - a personalized portrait generation framework combining customized image-generation models and face-related perceptual understanding models to generate truthful personalized portraits; it works with a handful of portrait images as input.                                                                                                                                                   | [Paper](https://arxiv.org/abs/2308.14256v1)                                                                             |
| 10) **Qwen-VL** - introduces a set of large-scale vision-language models demonstrating strong performance in tasks like image captioning, question answering, visual localization, and flexible interaction.                                                                                                                                                                                                      | [Paper](https://arxiv.org/abs/2308.12966), [Tweet](https://twitter.com/arankomatsuzaki/status/1695964537671893306?s=20) |

---

## Top ML Papers of the Week (August 21 - August 27)

| **Paper**                                                                                                                                                                                                                                                                                                                                                          | **Links**                                                                                                                                                           |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1) **Code Llama** - a family of LLMs for code based on Llama 2; the models provided as part of this release: foundation base models                                                                                                                                                                                                                                | [Paper](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/), [Tweet](https://twitter.com/MetaAI/status/1694729071325007993?s=20) |
| 2) **Survey on Instruction Tuning for LLMs** - new survey paper on instruction tuning LLM, including a systematic review of the literature, methodologies, dataset construction, training models, applications, and more.                                                                                                                                          | [Paper](https://arxiv.org/abs/2308.10792), [Tweet](https://twitter.com/omarsar0/status/1693978006237102589?s=20)                                                    |
| 3) **SeamlessM4T** - a unified multilingual and multimodal machine translation system that supports ASR, text-to-text translation, speech-to-text translation, text-to-speech translation, and speech-to-speech translation.                                                                                                                                       | [Paper](https://ai.meta.com/research/publications/seamless-m4t/), [Tweet](https://twitter.com/MetaAI/status/1694020437532151820?s=20)                               |
| 4) **Use of LLMs for Illicit Purposes** - provides an overview of existing efforts to identify and mitigate threats and vulnerabilities arising from LLMs; serves as a guide to building more reliable and robust LLM-powered systems.                                                                                                                             | [Paper](https://arxiv.org/abs/2308.12833), [Tweet](https://twitter.com/omarsar0/status/1694885393286549636?s=20)                                                    |
| 5) **Giraffe** - a new family of models that are fine-tuned from base Llama and Llama 2; extends the context length to 4K, 16K, and 32K; explores the space of expanding context lengths in LLMs so it also includes insights useful for practitioners and researchers.                                                                                            | [Paper](https://arxiv.org/abs/2308.10882), [Tweet](https://twitter.com/bindureddy/status/1694126931174977906?s=20)                                                  |
| 6) **IT3D** - presents a strategy that leverages explicitly synthesized multi-view images to improve Text-to-3D generation; integrates a discriminator along a Diffusion-GAN dual training strategy to guide the training of the 3D models.                                                                                                                        | [Paper](https://arxiv.org/abs/2308.11473v1)                                                                                                                         |
| 7) **A Survey on LLM-based Autonomous Agents** - presents a comprehensive survey of LLM-based autonomous agents; delivers a systematic review of the field and a summary of various applications of LLM-based AI agents in domains like social science and engineering.                                                                                            | [Paper](https://arxiv.org/abs/2308.11432v1), [Tweet](https://twitter.com/omarsar0/status/1695440652048257251?s=20)                                                  |
| 8) **Prompt2Model** - a new framework that accepts a prompt describing a task through natural language; it then uses the prompt to train a small special-purpose model that is conducive to deployment; the proposed pipeline automatically collects and synthesizes knowledge through three channels: dataset retrieval, dataset generation, and model retrieval. | [Paper](https://arxiv.org/abs/2308.12261), [Tweet](https://twitter.com/omarsar0/status/1694718168185598055?s=20)                                                    |
| 9) **LegalBench** - a collaboratively constructed benchmark for measuring legal reasoning in LLMs; it consists of 162 tasks covering 6 different types of legal reasoning.                                                                                                                                                                                         | [Paper](https://arxiv.org/abs/2308.11462), [Tweet](https://twitter.com/NeelGuha/status/1694375959334670643?s=20)                                                    |
| 10) **Language to Rewards for Robotic Skill Synthesis** - proposes a new language-to-reward system that utilizes LLMs to define optimizable reward parameters to achieve a variety of robotic tasks; the method is evaluated on a real robot arm where complex manipulation skills such as non-prehensile pushing emerge.                                          | [Paper](https://arxiv.org/abs/2306.08647), [Tweet](https://twitter.com/GoogleAI/status/1694086273689076170?s=20)                                                    |

---

## Top ML Papers of the Week (August 14 - August 20)

| **Paper**                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | **Links**                                                                                                                     |
|   | ----------------------------------------------------------------------------------------------------------------------------- |
| 1) **Self-Alignment with Instruction Backtranslation** - presents an approach to automatically label human-written text with corresponding instruction which enables building a high-quality instruction following language model; the steps are: 1) fine-tune an LLM with small seed data and web corpus, then 2) generate instructions for each web doc, 3) curate high-quality examples via the LLM, and finally 4) fine-tune on the newly curated data; the self-alignment approach outperforms all other Llama-based models on the Alpaca leaderboard. | [Paper](https://arxiv.org/abs/2308.06259), [Tweet](https://twitter.com/jaseweston/status/1690888779878330368?s=20)            |
| 2) **Platypus** - a family of fine-tuned and merged LLMs currently topping the Open LLM Leaderboard; it describes a process of efficiently fine-tuning and merging LoRA modules and also shows the benefits of collecting high-quality datasets for fine-tuning; specifically, it presents a small-scale, high-quality, and highly curated dataset, Open-Platypus, that enables strong performance with short and cheap fine-tuning time and cost... one can train a 13B model on a single A100 GPU using 25K questions in 5 hours.                         | [Paper](https://arxiv.org/abs/2308.07317v1), [Tweet](https://twitter.com/omarsar0/status/1692549762480791959?s=20)            |
| 3) **Model Compression for LLMs** - a short survey on the recent model compression techniques for LLMs; provides a high-level overview of topics such as quantization, pruning, knowledge distillation, and more; it also provides an overview of benchmark strategies and evaluation metrics for measuring the effectiveness of compressed LLMs.                                                                                                                                                                                                           | [Paper](https://arxiv.org/abs/2308.07633), [Tweet](https://twitter.com/omarsar0/status/1691803395160477905?s=20)              |
| 4) **GEARS** - uses deep learning and gene relationship knowledge graph to help predict cellular responses to genetic perturbation; GEARS exhibited 40% higher precision than existing approaches in the task of predicting four distinct genetic interaction subtypes in a combinatorial perturbation screen.                                                                                                                                                                                                                                              | [Paper](http://nature.com/articles/s41587-023-01905-6.pdf), [Tweet](https://twitter.com/jure/status/1692229511096754594?s=20) |
| 5) **Shepherd** - introduces a language model (7B) specifically tuned to critique the model responses and suggest refinements; this enables the capability to identify diverse errors and suggest remedies; its critiques are either similar or preferred to ChatGPT.                                                                                                                                                                                                                                                                                       | [Paper](https://arxiv.org/abs/2308.04592), [Tweet](https://twitter.com/MetaAI/status/1691517949130207232?s=20)                |
| 6) **Using GPT-4 Code Interpreter to Boost Mathematical Reasoning** - proposes a zero-shot prompting technique for GPT-4 Code Interpreter that explicitly encourages the use of code for self-verification which further boosts performance on math reasoning problems; initial experiments show that GPT4-Code achieved a zero-shot accuracy of 69.7% on the MATH dataset which is an improvement of 27.5% over GPT-4’s performance (42.2%). Lots to explore here.                                                                                         | [Paper](https://arxiv.org/abs/2308.07921), [Tweet](https://twitter.com/omarsar0/status/1691630591744127355?s=20)              |
| 7) **Teach LLMs to Personalize** - proposes a general approach based on multitask learning for personalized text generation using LLMs; the goal is to have an LLM generate personalized text without relying on predefined attributes.                                                                                                                                                                                                                                                                                                                     | [Paper](https://arxiv.org/abs/2308.07968), [Tweet](https://twitter.com/omarsar0/status/1692186726192521364?s=20)              |
| 8) **OctoPack** - presents 4 terabytes of Git commits across 350 languages used to instruction tune code LLMs; achieves state-of-the-art performance among models not trained on OpenAI outputs, on the HumanEval Python benchmark; the data is also used to extend the HumanEval benchmark to other tasks such as code explanation and code repair.                                                                                                                                                                                                        | [Paper](https://arxiv.org/abs/2308.07124v1), [Tweet](https://twitter.com/arankomatsuzaki/status/1691259656453193728?s=20)     |
| 9) **Efficient Guided Generation for LLMs** - presents a library to help LLM developers guide text generation in a fast and reliable way; provides generation methods that guarantee that the output will match a regular expression, or follow a JSON schema.                                                                                                                                                                                                                                                                                              | [Paper](https://arxiv.org/abs/2307.09702), [Tweet](https://twitter.com/omarsar0/status/1691179888214966273?s=20)              |
| 10) **Bayesian Flow Networks** - introduces a new class of generative models bringing together the power of Bayesian inference and deep learning; it differs from diffusion models in that it operates on the parameters of a data distribution rather than on a noisy version of the data; it’s adapted to continuous, discretized and discrete data with minimal changes to the training procedure.                                                                                                                                                       | [Paper](https://arxiv.org/abs/2308.07037), [Tweet](https://twitter.com/nnaisense/status/1691310494039379969?s=20)             |

---

## Top ML Papers of the Week (August 7 - August 13)

| **Paper**                                                                                                                                                                                                                                                                                                                                                                                                                              | **Links**                                                                                                                      |
| -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------ |
| 1) **LLMs as Database Administrators** - presents D-Bot, a framework based on LLMs that continuously acquires database maintenance experience from textual sources; D-Bot can help in performing: 1) database maintenance knowledge detection from documents and tools, 2) tree of thought reasoning for root cause analysis, and 3) collaborative diagnosis among multiple LLMs.                                                      | [Paper](https://arxiv.org/abs/2308.05481), [Tweet](https://twitter.com/omarsar0/status/1689811820272353280?s=20)               |
| 2) **Political Biases Found in NLP Models** - develops methods to measure media biases in LLMs, including the fairness of downstream NLP models tuned on top of politically biased LLMs; findings reveal that LLMs have political leanings which reinforce existing polarization in the corpora.                                                                                                                                       | [Paper](https://aclanthology.org/2023.acl-long.656/), [Tweet](https://twitter.com/AiBreakfast/status/1688939983468453888?s=20) |
| 3) **Evaluating LLMs as Agents** - presents a multidimensional benchmark (AgentBench) to assess LLM-as-Agent’s reasoning and decision-making abilities; results show that there is a significant disparity in performance between top commercial LLMs and open-source LLMs when testing the ability to act as agents; open-source LLMs lag on the AgentBench tasks while GPT-4 shows potential to build continuously learning agents.  | [Paper](https://arxiv.org/abs/2308.03688v1), [Tweet](https://twitter.com/arankomatsuzaki/status/1688719837760000000?s=20)      |
| 4) **Studying LLM Generalization with Influence Functions** - introduces an efficient approach to scale influence functions to LLMs with up to 52 billion parameters; the influence functions are used to further investigate the generalization patterns of LLMs such as cross-lingual generalization and memorization; finds that middle layers in the network seem to be responsible for the most abstract generalization patterns. | [Paper](https://arxiv.org/abs/2308.03296), [Tweet](https://twitter.com/AnthropicAI/status/1688946685937090560?s=20)            |
| 5) **Seeing Through the Brain** - proposes NeuroImagen, a pipeline for reconstructing visual stimuli images from EEG signals to potentially understand visually-evoked brain activity; a latent diffusion model takes EEG data and reconstructs high-resolution visual stimuli images.                                                                                                                                                 | [Paper](https://arxiv.org/abs/2308.02510), [Tweet](https://twitter.com/_akhaliq/status/1688787286807228416?s=20)               |
| 6) **SynJax** - is a new library that provides an efficient vectorized implementation of inference algorithms for structured distributions; it enables building large-scale differentiable models that explicitly model structure in data like tagging, segmentation, constituency trees, and spanning trees.                                                                                                                          | [Paper](https://arxiv.org/abs/2308.03291v1), [Tweet](https://twitter.com/milosstanojevic/status/1688896558790520832?s=20)      |
| 7) **Synthetic Data Reduces Sycophancy in LLMs** - proposes fine-tuning on simple synthetic data to reduce sycophancy in LLMs; sycophancy occurs when LLMs try to follow a user’s view even when it’s not objectively correct; essentially, the LLM repeats the user’s view even when the opinion is wrong.                                                                                                                            | [Paper](https://arxiv.org/abs/2308.03958), [Tweet](https://twitter.com/JerryWeiAI/status/1689340237993185280?s=20)             |
| 8) **Photorealistic Unreal Graphics (PUG)** - presents photorealistic and semantically controllable synthetic datasets for representation learning using Unreal Engine; the goal is to democratize photorealistic synthetic data and enable more rigorous evaluations of vision models.                                                                                                                                                | [Paper](https://arxiv.org/abs/2308.03977), [Tweet](https://twitter.com/MetaAI/status/1689316127846109184?s=20)                 |
| 9) **LLMs for Industrial Control** - develops an approach to select demonstrations and generate high-performing prompts used with GPT for executing tasks such as controlling (Heating, Ventilation, and Air Conditioning) for buildings; GPT-4 performs comparable to RL method but uses fewer samples and lower technical debt.                                                                                                      | [Paper](https://arxiv.org/abs/2308.03028), [Tweet](https://twitter.com/emollick/status/1688760539441217536?s=20)               |
| 10) **Trustworthy LLMs** - presents a comprehensive overview of important categories and subcategories crucial for assessing LLM trustworthiness; the dimensions include reliability, safety, fairness, resistance to misuse, explainability and reasoning, adherence to social norms, and robustness; finds that aligned models perform better in terms of trustworthiness but the effectiveness of alignment varies.                 | [Paper](https://arxiv.org/abs/2308.05374), [Tweet](https://twitter.com/_akhaliq/status/1689818964669390848?s=20)               |

---

## Top ML Papers of the Week (July 31 - August 6)

| **Paper**                                                                                                                                                                                                                                                                                                                                                                                                    | **Links**                                                                                                               |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------- |
| 1) **Open Problem and Limitation of RLHF** - provides an overview of open problems and the limitations of RLHF.                                                                                                                                                                                                                                                                                              | [Paper](https://arxiv.org/abs/2307.15217), [Tweet](https://twitter.com/arankomatsuzaki/status/1685813753063870465?s=20) |
| 2) **Med-Flamingo** - a new multimodal model that allows in-context learning and enables tasks such as few-shot medical visual question answering; evaluations based on physicians, show improvements of up to 20% in clinician's rating; the authors occasionally observed low-quality generations and hallucinations.                                                                                      | [Paper](https://arxiv.org/abs/2307.15189), [Tweet](https://twitter.com/Michael_D_Moor/status/1685804620730540033?s=20)  |
| 3) **ToolLLM** - enables LLMs to interact with 16000 real-world APIs; it’s a framework that allows data preparation, training, and evaluation; the authors claim that one of their models, ToolLLaMA, has reached the performance of ChatGPT (turbo-16k) in tool use.                                                                                                                                        | [Paper](https://arxiv.org/abs/2307.16789v1), [Tweet](https://twitter.com/omarsar0/status/1687531613574348800?s=20)      |
| 4) **Skeleton-of-Thought** - proposes a prompting strategy that firsts generate an answer skeleton and then performs parallel API calls to generate the content of each skeleton point; reports quality improvements in addition to speed-up of up to 2.39x.                                                                                                                                                 | [Paper](https://arxiv.org/abs/2307.15337), [Tweet](https://twitter.com/omarsar0/status/1685832487103008768?s=20)        |
| 5) **MetaGPT** - a framework involving LLM-based multi-agents that encodes human standardized operating procedures (SOPs) to extend complex problem-solving capabilities that mimic efficient human workflows; this enables MetaGPT to perform multifaceted software development, code generation tasks, and even data analysis using tools like AutoGPT and LangChain.                                      | [Paper](https://arxiv.org/abs/2308.00352v2), [Tweet](https://twitter.com/ai_database/status/1686949868298973184?s=20)   |
| 6) **OpenFlamingo** - introduces a family of autoregressive vision-language models ranging from 3B to 9B parameters; the technical report describes the models, training data, and evaluation suite.                                                                                                                                                                                                         | [Paper](https://arxiv.org/abs/2308.01390), [Tweet](https://twitter.com/anas_awadalla/status/1687295129005195264?s=20)   |
| 7) **The Hydra Effect** - shows that language models exhibit self-repairing properties — when one layer of attention heads is ablated it causes another later layer to take over its function.                                                                                                                                                                                                               | [Paper](https://arxiv.org/abs/2307.15771), [Tweet](https://twitter.com/_akhaliq/status/1686192437771788288?s=20)        |
| 8) **Self-Check** - explores whether LLMs have the capability to perform self-checks which is required for complex tasks that depend on non-linear thinking and multi-step reasoning; it proposes a zero-shot verification scheme to recognize errors without external resources; the scheme can improve question-answering performance through weighting voting and even improve math word problem-solving. | [Paper](https://arxiv.org/abs/2308.00436), [Tweet](https://twitter.com/_akhaliq/status/1686561569486827520?s=20)        |
| 9) **Agents Model the World with Language** - presents an agent that learns a multimodal world model that predicts future text and image representations; it learns to predict future language, video, and rewards; it’s applied to different domains and can learn to follow instructions in visually and linguistically complex domains.                                                                   | [Paper](https://arxiv.org/abs/2308.01399), [Tweet](https://twitter.com/johnjnay/status/1687277999517818880?s=20)        |
| 10) **AutoRobotics-Zero** - discovers zero-shot adaptable policies from scratch that enable adaptive behaviors necessary for sudden environmental changes; as an example, the authors demonstrate the automatic discovery of Python code for controlling a robot.                                                                                                                                            | [Paper](https://arxiv.org/abs/2307.16890), [Tweet](https://twitter.com/XingyouSong/status/1686190266578046976?s=20)     |

---

## Top ML Papers of the Week (July 24 - July 30)

| **Paper**                                                                                                                                                                                                                                                                                         | **Links**                                                                                                                                    |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------- |
| 1) **Universal Adversarial LLM Attacks** - finds universal and transferable adversarial attacks that cause aligned models like ChatGPT and Bard to generate objectionable behaviors; the approach automatically produces adversarial suffixes using greedy and gradient search.                   | [Paper](https://arxiv.org/abs/2307.15043), [Tweet](https://twitter.com/andyzou_jiaming/status/1684766170766004224?s=20)                      |
| 2) **RT-2** - a new end-to-end vision-language-action model that learns from both web and robotics data; enables the model to translate the learned knowledge to generalized instructions for robotic control.                                                                                    | [Paper](https://robotics-transformer2.github.io/assets/rt2.pdf), [Tweet](https://twitter.com/GoogleDeepMind/status/1684903412834447360?s=20) |
| 3) **Med-PaLM Multimodal** - introduces a new multimodal biomedical benchmark with 14 different tasks; it presents a proof of concept for a generalist biomedical AI system called Med-PaLM Multimodal; it supports different types of biomedical data like clinical text, imaging, and genomics. | [Paper](https://arxiv.org/abs/2307.14334), [Tweet](https://twitter.com/vivnat/status/1684404882844024832?s=20)                               |
| 4) **Tracking Anything in High Quality** - propose a framework for high-quality tracking anything in videos; consists of a video multi-object segmented and a pretrained mask refiner model to refine the tracking results; the model ranks 2nd place in the VOTS2023 challenge.                  | [Paper](https://arxiv.org/abs/2307.13974v1), [Tweet](https://twitter.com/arankomatsuzaki/status/1684380610901467136?s=20)                    |
| 5) **Foundation Models in Vision** - presents a survey and outlook discussing open challenges and research directions for foundational models in computer vision.                                                                                                                                 | [Paper](https://arxiv.org/abs/2307.13721v1), [Tweet](https://twitter.com/KhanSalmanH/status/1684496991215316992?s=20)                        |
| 6) **L-Eval** - a standardized evaluation for long context language models containing 411 long documents over 2K query-response pairs encompassing areas such as law, finance, school lectures, long conversations, novels, and meetings.                                                         | [Paper](https://arxiv.org/abs/2307.11088v1), [Tweet](https://twitter.com/WenxiangJiao/status/1682208555762610176?s=20)                       |
| 7) **LoraHub** - introduces LoraHub to enable efficient cross-task generalization via dynamic LoRA composition; it enables the combination of LoRA modules without human expertise or additional parameters/gradients; mimics the performance of in-context learning in few-shot scenarios.       | [Paper](https://arxiv.org/abs/2307.13269v1), [Tweet](https://twitter.com/_akhaliq/status/1684030297661403136?s=20)                           |
| 8) **Survey of Aligned LLMs** - resents a comprehensive overview of alignment approaches, including aspects like data collection, training methodologies, and model evaluation.                                                                                                                   | [Paper](https://arxiv.org/abs/2307.12966v1), [Tweet](https://twitter.com/omarsar0/status/1684960627423420419?s=20)                           |
| 9) **WavJourney** - leverages LLMs to connect various audio models to compose audio content for engaging storytelling; this involves an explainable and interactive design that enhances creative control in audio production.                                                                    | [Paper](https://arxiv.org/abs/2307.14335v1), [Tweet](https://twitter.com/LiuXub/status/1684338437934002176?s=20)                             |
| 10) **FacTool** - a task and domain agnostic framework for factuality detection of text generated by LLM; the effectiveness of the approach is tested on tasks such as code generation and mathematical reasoning; a benchmark dataset is released, including a ChatGPT plugin.                   | [Paper](https://arxiv.org/abs/2307.13528v2), [Tweet](https://twitter.com/gneubig/status/1684658613921669120?s=20)                            |

---

## Top ML Papers of the Week (July 17 - July 23)

| **Paper**                                                                                                                                                                                                                                                                                                                                                                     | **Links**                                                                                                                                                                                    |
| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1) **Llama 2** - a collection of pretrained foundational models and fine-tuned chat models ranging in scale from 7B to 70B; Llama 2-Chat is competitive on a range of tasks and shows strong results on safety and helpfulness.                                                                                                                                               | [Paper](https://arxiv.org/abs/2307.09288v2), [Tweet](https://twitter.com/MetaAI/status/1681363272484945921?s=20)                                                                             |
| 2) **How is ChatGPT’s Behavior Changing Over Time?** - evaluates different versions of GPT-3.5 and GPT-4 on various tasks and finds that behavior and performance vary greatly over time; this includes differences in performance for tasks such as math problem-solving, safety-related generations, and code formatting.                                                   | [Paper](https://arxiv.org/abs/2307.09009v1), [Tweet](https://twitter.com/matei_zaharia/status/1681467961905926144?s=20)                                                                      |
| 3) **FlashAttention-2** - improves work partitioning and parallelism and addresses issues like reducing non-matmul FLOPs, parallelizing attention computation which increases occupancy, and reducing communication through shared memory.                                                                                                                                    | [Paper](https://arxiv.org/abs/2307.08691v1), [Tweet](https://twitter.com/tri_dao/status/1680987577913065472?s=20)                                                                            |
| 4) **Measuring Faithfulness in Chain-of-Thought Reasoning** - nds that CoT reasoning shows large variation across tasks by simple interventions like adding mistakes and paraphrasing; demonstrates that as the model becomes larger and more capable, the reasoning becomes less faithful; suggests carefully choosing the model size and tasks can enable CoT faithfulness. | [Paper](https://www-files.anthropic.com/production/files/measuring-faithfulness-in-chain-of-thought-reasoning.pdf), [Tweet](https://twitter.com/AnthropicAI/status/1681341063083229189?s=20) |
| 5) **Generative TV & Showrunner Agents** - an approach to generate episodic content using LLMs and multi-agent simulation; this enables current systems to perform creative storytelling through the integration of simulation, the user, and powerful AI models and enhance the quality of AI-generated content.                                                             | [Paper](https://fablestudio.github.io/showrunner-agents/), [Tweet](https://twitter.com/fablesimulation/status/1681352904152850437?s=20)                                                      |
| 6) **Challenges & Application of LLMs** - summarizes a comprehensive list of challenges when working with LLMs that range from brittle evaluations to prompt brittleness to a lack of robust experimental designs.                                                                                                                                                            | [Paper](https://arxiv.org/abs/2307.10169), [Tweet](https://twitter.com/omarsar0/status/1681844380934500358?s=20)                                                                             |
| 7) **Retentive Network** - presents a foundation architecture for LLMs with the goal to improve training efficiency, inference, and efficient long-sequence modeling; adapts retention mechanism for sequence modeling that support parallel representation, recurrent representations, and chunkwise recurrent representation.                                               | [Paper](https://arxiv.org/abs/2307.08621), [Tweet](https://twitter.com/arankomatsuzaki/status/1681113977500184576?s=20)                                                                      |
| 8) **Meta-Transformer** - a framework that performs unified learning across 12 modalities; it can handle tasks that include fundamental perception (text, image, point cloud, audio, video), practical application (X-Ray, infrared, hyperspectral, and IMU), and data mining (graph, tabular, and time-series).                                                              | [Paper](https://arxiv.org/abs/2307.10802), [Tweet](https://twitter.com/omarsar0/status/1682197751990288385?s=20)                                                                             |
| 9) **Retrieve In-Context Example for LLMs** - presents a framework to iteratively train dense retrievers to identify high-quality in-context examples for LLMs; the approach enhances in-context learning performance demonstrated using a suite of 30 tasks; examples with similar patterns are helpful and gains are consistent across model sizes.                         | [Paper](https://arxiv.org/abs/2307.07164), [Tweet](https://twitter.com/_akhaliq/status/1680770636166094848?s=20)                                                                             |
| 10) **FLASK** - proposes fine-grained evaluation for LLMs based on a range of alignment skill sets; involves 12 skills and can help to provide a holistic view of a model’s performance depending on skill, domain, and level of difficulty; useful to analyze factors that make LLMs more proficient at specific skills.                                                     | [Paper](https://arxiv.org/abs/2307.10928), [Tweet](https://twitter.com/SeonghyeonYe/status/1682209670302408705?s=20)                                                                         |

---

## Top ML Papers of the Week (July 10 - July 16)

| **Paper**                                                                                                                                                                                                                                                                                                | **Links**                                                                                                                                                                                             |
| -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1) **CM3Leon** - introduces a retrieval-augmented multi-modal language model that can generate text and images; leverages diverse and large-scale instruction-style data for tuning which leads to significant performance improvements and 5x less training compute than comparable methods.            | [Paper](https://ai.meta.com/research/publications/scaling-autoregressive-multi-modal-models-pretraining-and-instruction-tuning/), [Tweet](https://twitter.com/MetaAI/status/1679885986363478018?s=20) |
| 2) **Claude 2** - presents a detailed model card for Claude 2 along with results on a range of safety, alignment, and capabilities evaluations.                                                                                                                                                          | [Paper](https://www-files.anthropic.com/production/images/Model-Card-Claude-2.pdf), [Tweet](https://twitter.com/AnthropicAI/status/1678759122194530304?s=20)                                          |
| 3) **Secrets of RLHF in LLMs** - takes a closer look at RLHF and explores the inner workings of PPO with code included.                                                                                                                                                                                  | [Paper](https://arxiv.org/abs/2307.04964), [Tweet](https://twitter.com/omarsar0/status/1678938028918571009?s=20)                                                                                      |
| 4) **LongLLaMA** - employs a contrastive training process to enhance the structure of the (key, value) space to extend context length; presents a fine-tuned model that lengthens context and demonstrates improvements in long context tasks.                                                           | [Paper](https://arxiv.org/abs/2307.03170v1), [Tweet](https://twitter.com/s_tworkowski/status/1677125863429795840?s=20)                                                                                |
| 5) **Patch n’ Pack: NaViT** - introduces a vision transformer for any aspect ratio and resolution through sequence packing; enables flexible model usage, improved training efficiency, and transfers to tasks involving image and video classification among others.                                    | [Paper](https://arxiv.org/abs/2307.06304), [Tweet](https://twitter.com/m__dehghani/status/1679558751248850969?s=20)                                                                                   |
| 6) **LLMs as General Pattern Machines** - shows that even without any additional training, LLMs can serve as general sequence modelers, driven by in-context learning; this work applies zero-shot capabilities to robotics and shows that it’s possible to transfer the pattern among words to actions. | [Paper](https://arxiv.org/abs/2307.04721), [Tweet](https://twitter.com/DrJimFan/status/1679898692307005440?s=20)                                                                                      |
| 7) **HyperDreamBooth** - introduces a smaller, faster, and more efficient version of Dreambooth; enables personalization of text-to-image diffusion model using a single input image, 25x faster than Dreambooth.                                                                                        | [Paper](https://arxiv.org/abs/2307.06949), [Tweet](https://twitter.com/natanielruizg/status/1679893292618752000?s=20)                                                                                 |
| 8) **Teaching Arithmetics to Small Transformers** - trains small transformer models on chain-of-thought style data to significantly improve accuracy and convergence speed; it highlights the importance of high-quality instructive data for rapidly eliciting arithmetic capabilities.                 | [Paper](https://arxiv.org/abs/2307.03381), [Tweet](https://twitter.com/DimitrisPapail/status/1678407512637284352?s=20)                                                                                |
| 9) **AnimateDiff** - appends a motion modeling module to a frozen text-to-image model, which is then trained and used to animate existing personalized models to produce diverse and personalized animated images.                                                                                       | [Paper](https://arxiv.org/abs/2307.04725v1), [Tweet](https://twitter.com/dreamingtulpa/status/1679459297946632193?s=20)                                                                               |
| 10) **Generative Pretraining in Multimodality** - presents a new transformer-based multimodal foundation model to generate images and text in a multimodal context; enables performant multimodal assistants via instruction tuning.                                                                     | [Paper](https://arxiv.org/abs/2307.05222v1), [Tweet](https://twitter.com/_akhaliq/status/1678939405170475008?s=20)                                                                                    |

---

## Top ML Papers of the Week (July 3 - July 9)

| **Paper**                                                                                                                                                                                                                                                                                                  | **Links**                                                                                                               |
| ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
| 1) **A Survey on Evaluation of LLMs** - a comprehensive overview of evaluation methods for LLMs focusing on what to evaluate, where to evaluate, and how to evaluate.                                                                                                                                      | [Paper](https://arxiv.org/abs/2307.03109), [Tweet](https://twitter.com/omarsar0/status/1677137934946803712?s=20)        |
| 2) **How Language Models Use Long Contexts** - finds that LM performance is often highest when relevant information occurs at the beginning or end of the input context; performance degrades when relevant information is provided in the middle of a long context.                                       | [Paper](https://arxiv.org/abs/2307.03172), [Tweet](https://twitter.com/nelsonfliu/status/1677373731948339202?s=20)      |
| 3) **LLMs as Effective Text Rankers** - proposes a prompting technique that enables open-source LLMs to perform state-of-the-art text ranking on standard benchmarks.                                                                                                                                      | [Paper](https://arxiv.org/abs/2306.17563), [Tweet](https://twitter.com/arankomatsuzaki/status/1675673784454447107?s=20) |
| 4) **Multimodal Generation with Frozen LLMs** - introduces an approach that effectively maps images to the token space of LLMs; enables models like PaLM and GPT-4 to tackle visual tasks without parameter updates; enables multimodal tasks and uses in-context learning to tackle various visual tasks. | [Paper](https://arxiv.org/abs/2306.17842), [Tweet](https://twitter.com/roadjiang/status/1676375112914989056?s=20)       |
| 5) **CodeGen2.5** - releases a new code LLM trained on 1.5T tokens; the 7B model is on par with >15B code-generation models and it’s optimized for fast sampling.                                                                                                                                          | [Paper](https://arxiv.org/abs/2305.02309), [Tweet](https://twitter.com/erik_nijkamp/status/1677055271104045056?s=20)    |
| 6) **Elastic Decision Transformer** - introduces an advancement over Decision Transformers and variants by facilitating trajectory stitching during action inference at test time, achieved by adjusting to shorter history that allows transitions to diverse and better future states.                   | [Paper](https://arxiv.org/abs/2307.02484), [Tweet](https://twitter.com/xiaolonw/status/1677003542249484289?s=20)        |
| 7) **Robots That Ask for Help** - presents a framework to measure and align the uncertainty of LLM-based planners that ask for help when needed.                                                                                                                                                           | [Paper](https://arxiv.org/abs/2307.01928), [Tweet](https://twitter.com/allenzren/status/1677000811803443213?s=20)       |
| 8) **Physics-based Motion Retargeting in Real-Time** - proposes a method that uses reinforcement learning to train a policy to control characters in a physics simulator; it retargets motions in real-time from sparse human sensor data to characters of various morphologies.                           | [Paper](https://arxiv.org/abs/2307.01938), [Tweet](https://twitter.com/_akhaliq/status/1676822600478015488?s=20)        |
| 9) **Scaling Transformer to 1 Billion Tokens** - presents LongNet, a Transformer variant that can scale sequence length to more than 1 billion tokens, with no loss in shorter sequences.                                                                                                                  | [Paper](https://arxiv.org/abs/2307.02486), [Tweet](https://twitter.com/arankomatsuzaki/status/1676765133362675712?s=20) |
| 10) **InterCode** - introduces a framework of interactive coding as a reinforcement learning environment; this is different from the typical coding benchmarks that consider a static sequence-to-sequence process.                                                                                        | [Paper](https://arxiv.org/abs/2306.14898), [Tweet](https://twitter.com/ShunyuYao12/status/1675903408727896066?s=20)     |

---

## Top ML Papers of the Week (June 26 - July 2)

| **Paper**                                                                                                                                                                                                                                                                                                                       | **Links**                                                                                                                |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------ |
| 1) **LeanDojo** - an open-source Lean playground consisting of toolkits, data, models, and benchmarks for theorem proving; also develops ReProver, a retrieval augmented LLM-based prover for theorem solving using premises from a vast math library.                                                                          | [Paper](https://arxiv.org/abs/2306.15626), [Tweet](https://twitter.com/KaiyuYang4/status/1673882824158613504?s=20)       |
| 2) **Extending Context Window of LLMs** - extends the context window of LLMs like LLaMA to up to 32K with minimal fine-tuning (within 1000 steps); previous methods for extending the context window are inefficient but this approach attains good performance on several tasks while being more efficient and cost-effective. | [Paper](https://arxiv.org/abs/2306.15595), [Tweet](https://twitter.com/omarsar0/status/1674073189800919042?s=20)         |
| 3) **Computer Vision Through the Lens of Natural Language** - proposes a modular approach for solving computer vision problems by leveraging LLMs; the LLM is used to reason over outputs from independent and descriptive modules that provide extensive information about an image.                                           | [Paper](https://arxiv.org/abs/2306.16410), [Tweet](https://twitter.com/arankomatsuzaki/status/1674219223856365569?s=20)  |
| 4) **Visual Navigation Transformer** - a foundational model that leverages the power of pretrained models to vision-based robotic navigation; it can be used with any navigation dataset and is built on a flexible Transformer-based architecture that can tackle various navigational tasks.                                  | [Paper](https://arxiv.org/abs/2306.14846), [Tweet](https://twitter.com/svlevine/status/1673732522155601920?s=20)         |
| 5) **Generative AI for Programming Education** - evaluates GPT-4 and ChatGPT on programming education scenarios and compares their performance with human tutors; GPT-4 outperforms ChatGPT and comes close to human tutors' performance.                                                                                       | [Paper](https://arxiv.org/abs/2306.17156), [Tweet](https://twitter.com/_akhaliq/status/1674590713051242498?s=20)         |
| 6) **DragDiffusion** - extends interactive point-based image editing using diffusion models; it optimizes the diffusion latent to achieve precise spatial control and complete high-quality editing efficiently.                                                                                                                | [Paper](https://arxiv.org/abs/2306.14435), [Tweet](https://twitter.com/_akhaliq/status/1673570232429051906?s=20)         |
| 7) **Understanding Theory-of-Mind in LLMs with LLMs** - a framework for procedurally generating evaluations with LLMs; proposes a benchmark to study the social reasoning capabilities of LLMs with LLMs.                                                                                                                       | [Paper](https://arxiv.org/abs/2306.15448), [Tweet](https://twitter.com/johnjnay/status/1673871545725505537?s=20)         |
| 8) **Evaluations with No Labels** - a framework for self-supervised evaluation of LLMs by analyzing their sensitivity or invariance to transformations on input text; can be used to monitor LLM behavior on datasets streamed during live model deployment.                                                                    | [Paper](https://arxiv.org/abs/2306.13651v1), [Tweet](https://twitter.com/tomgoldsteincs/status/1673808766679097346?s=20) |
| 9) **Long-range Language Modeling with Self-Retrieval** - an architecture and training procedure for jointly training a retrieval-augmented language model from scratch for long-range language modeling tasks.                                                                                                                 | [Paper](https://arxiv.org/abs/2306.13421), [Tweet](https://twitter.com/arankomatsuzaki/status/1673129191863140353?s=20)  |
| 10) **Scaling MLPs: A Tale of Inductive Bias** - shows that the performance of MLPs improves with scale and highlights that lack of inductive bias can be compensated.                                                                                                                                                          | [Paper](https://arxiv.org/abs/2306.13575), [Tweet](https://twitter.com/ethanCaballero/status/1673725211907182592?s=20)   |

---

## Top ML Papers of the Week (June 19 - June 25)

| **Paper**                                                                                                                                                                                                                                                                                                                     | **Links**                                                                                                                 |
| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------- |
| 1) **Textbooks Are All You Need** - introduces a new 1.3B parameter LLM called phi-1; it’s significantly smaller in size and trained for 4 days using a selection of textbook-quality data and synthetic textbooks and exercises with GPT-3.5; achieves promising results on the HumanEval benchmark.                         | [Paper](https://arxiv.org/abs/2306.11644), [Tweet](https://twitter.com/SebastienBubeck/status/1671326369626853376?s=20)   |
| 2) **RoboCat** - a new foundation agent that can operate different robotic arms and can solve tasks from as few as 100 demonstrations; the self-improving AI agent can self-generate new training data to improve its technique and get more efficient at adapting to new tasks.                                              | [Paper](https://arxiv.org/abs/2306.11706), [Tweet](https://twitter.com/DeepMind/status/1671171448638144515?s=20)          |
| 3) **ClinicalGPT** - a language model optimized through extensive and diverse medical data, including medical records, domain-specific knowledge, and multi-round dialogue consultations.                                                                                                                                     | [Paper](https://arxiv.org/abs/2306.09968), [Tweet](https://twitter.com/omarsar0/status/1670606068777381890?s=20)          |
| 4) **An Overview of Catastrophic AI Risks** - provides an overview of the main sources of catastrophic AI risks; the goal is to foster more understanding of these risks and ensure AI systems are developed in a safe manner.                                                                                                | [Paper](https://arxiv.org/abs/2306.12001v1), [Tweet](https://twitter.com/DanHendrycks/status/1671894767331061763?s=20)    |
| 5) **LOMO** - proposes a new memory-efficient optimizer that combines gradient computation and parameter update in one step; enables tuning the full parameters of an LLM with limited resources.                                                                                                                             | [Paper](https://arxiv.org/abs/2306.09782), [Tweet](https://twitter.com/arankomatsuzaki/status/1670603218659811330?s=20)   |
| 6) **SequenceMatch** - formulates sequence generation as an imitation learning problem; this framework allows the ability to incorporate backtracking into text generation through a backspace action; this enables the generative model to mitigate compounding errors by reverting sample tokens that lead to sequence OOD. | [Paper](https://arxiv.org/abs/2306.05426), [Tweet](https://twitter.com/abacaj/status/1671636061494059009?s=20)            |
| 7) **LMFlow** - an extensible and lightweight toolkit that simplifies finetuning and inference of general large foundation models; supports continuous pretraining, instruction tuning, parameter-efficient finetuning, alignment tuning, and large model inference.                                                          | [Paper](https://arxiv.org/abs/2306.12420), [Tweet](https://twitter.com/omarsar0/status/1671881864930549761?s=20)          |
| 8) **MotionGPT** - uses multimodal control signals for generating consecutive human motions; it quantizes multimodal control signals intro discrete codes which are converted to LLM instructions that generate motion answers.                                                                                               | [Paper](https://arxiv.org/abs/2306.10900v1), [Tweet](https://twitter.com/arankomatsuzaki/status/1671341916980490241?s=20) |
| 9) **Wanda** - introduces a simple and effective pruning approach for LLMs; it prunes weights with the smallest magnitudes multiplied by the corresponding input activations, on a per-output basis; the approach requires no retraining or weight update and outperforms baselines of magnitude pruning.                     | [Paper](https://arxiv.org/abs/2306.11695), [Tweet](https://twitter.com/Yampeleg/status/1671885220218560516?s=20)          |
| 10) **AudioPaLM** - fuses text-based and speech-based LMs, PaLM-2 and AudioLM, into a multimodal architecture that supports speech understanding and generation; outperforms existing systems for speech translation tasks with zero-shot speech-to-text translation capabilities.                                            | [Paper](https://arxiv.org/abs/2306.12925v1), [Tweet](https://twitter.com/PaulKRubenstein/status/1672128984220413953?s=20) |

---

## Top ML Papers of the Week (June 12 - June 18)

| **Paper**                                                                                                                                                                                                                                                                                                                                 | **Links**                                                                                                                                                                                        |
| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| 1) **Voicebox** - an all-in-one generative speech model; it can synthesize speech across 6 languages; it can perform noise removal, content editing, style conversion, and more; it's 20x faster than current models and outperforms single-purpose models through in-context learning.                                                   | [Paper](https://research.facebook.com/publications/voicebox-text-guided-multilingual-universal-speech-generation-at-scale/), [Tweet](https://twitter.com/MetaAI/status/1669766837981306880?s=20) |
| 2) **FinGPT** - an open-source LLM for the finance sector; it takes a data-centric approach, providing researchers & practitioners with accessible resources to develop FinLLMs.                                                                                                                                                          | [Paper](https://arxiv.org/abs/2306.06031), [Tweet](https://twitter.com/omarsar0/status/1668060502663077891?s=20)                                                                                 |
| 3) **Crowd Workers Widely Use Large Language Models for Text Production Tasks** - estimates that 33-46% of crowd workers on MTurk used LLMs when completing a text production task.                                                                                                                                                       | [Paper](https://arxiv.org/abs/2306.07899v1), [Tweet](https://twitter.com/manoelribeiro/status/1668986074801098754?s=20)                                                                          |
| 4) **Reliability of Watermarks for LLMs** - watermarking is useful to detect LLM-generated text and potentially mitigate harms; this work studies the reliability of watermarking for LLMs and finds that watermarks are detectable even when the watermarked text is re-written by humans or paraphrased by another non-watermarked LLM. | [Paper](https://arxiv.org/abs/2306.04634), [Tweet](https://twitter.com/tomgoldsteincs/status/1668668484975464448?s=20)                                                                           |
| 5) **Applications of Transformers** - a new survey paper highlighting major applications of Transformers for deep learning tasks; includes a comprehensive list of Transformer models.                                                                                                                                                    | [Paper](https://arxiv.org/abs/2306.07303), [Tweet](https://twitter.com/omarsar0/status/1668989324950491139?s=20)                                                                                 |
| 6) **Benchmarking NN Training Algorithms** - it’s currently challenging to properly assess the best optimizers to train neural networks; this paper presents a new benchmark, AlgoPerf, for benchmarking neural network training algorithms using realistic workloads.                                                                    | [Paper](https://arxiv.org/abs/2306.07179), [Tweet](https://twitter.com/zacharynado/status/1668683433944424448?s=20)                                                                              |
| 7) **Unifying LLMs & Knowledge Graphs** - provides a roadmap for the unification of LLMs and KGs; covers how to incorporate KGs in LLM pre-training/inferencing, leverage LLMs for KG tasks such as question answering, and enhance both KGs and LLMs for bidirectional reasoning.                                                        | [Paper](https://arxiv.org/abs/2306.09310), [Tweet](https://twitter.com/johnjnay/status/1670051081722769408?s=20)                                                                                 |
| 8) **Augmenting LLMs with Long-term Memory** - proposes a framework to enable LLMs to memorize long history; it’s enhanced with memory-augmented adaptation training to memorize long past context and use long-term memory for language modeling; achieves improvements on memory-augmented in-context learning over LLMs.               | [Paper](https://arxiv.org/abs/2306.07174), [Tweet](https://twitter.com/arankomatsuzaki/status/1668429602841317378?s=20)                                                                          |
| 9) **TAPIR** - enables tracking any queried point on any physical surface throughout a video sequence; outperforms all baselines and facilitates fast inference on long and high-resolution videos (track points faster than real-time when using modern GPUs).                                                                           | [Paper](https://arxiv.org/abs/2306.08637), [Tweet](https://twitter.com/AdamWHarley/status/1669785589246468096?s=20)                                                                              |
| 10) **Mind2Web** - a new dataset for evaluating generalist agents for the web; contains 2350 tasks from 137 websites over 31 domains; it enables testing generalization ability across tasks and environments, covering practical use cases on the web.                                                                                   | [Paper](https://arxiv.org/abs/2306.06070), [Tweet](https://twitter.com/DrJimFan/status/1669403956064432128?s=20)                                                                                 |

---

## Top ML Papers of the Week (June 5 - June 11)

| **Paper**                                                                                                                                                                                                                                                                                                                      | **Links**                                                                                                                          |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------- |
| 1) **Tracking Everything Everywhere All at Once** - propose a test-time optimization method for estimating dense and long-range motion; enables accurate, full-length motion estimation of every pixel in a video.                                                                                                             | [Paper](https://arxiv.org/abs/2306.05422), [Tweet](https://twitter.com/sstj389/status/1667000331958468608?s=20)                    |
| 2) **AlphaDev** - a deep reinforcement learning agent which discovers faster sorting algorithms from scratch; the algorithms outperform previously known human benchmarks and have been integrated into the LLVM C++ library.                                                                                                  | [Paper](https://www.nature.com/articles/s41586-023-06004-9), [Tweet](https://twitter.com/omarsar0/status/1666486491793481738?s=20) |
| 3) **Sparse-Quantized Representation** - a new compressed format and quantization technique that enables near-lossless compression of LLMs across model scales; “allows LLM inference at 4.75 bits with a 15% speedup”.                                                                                                        | [Paper](https://arxiv.org/abs/2306.03078), [Tweet](https://twitter.com/Tim_Dettmers/status/1666076553665744896?s=20)               |
| 4) **MusicGen** - a simple and controllable model for music generation built on top of a single-stage transformer LM together with efficient token interleaving patterns; it can be conditioned on textual descriptions or melodic features and shows high performance on a standard text-to-music benchmark.                  | [Paper](https://arxiv.org/abs/2306.05284), [Tweet](https://twitter.com/syhw/status/1667103478471176192?s=20)                       |
| 5) **Augmenting LLMs with Databases** - combines an LLM with a set of SQL databases, enabling a symbolic memory framework; completes tasks via LLM generating SQL instructions that manipulate the DB autonomously.                                                                                                            | [Paper](https://arxiv.org/abs/2306.03901), [Tweet](https://twitter.com/omarsar0/status/1666254609524961282?s=20)                   |
| 6) **Concept Scrubbing in LLM** - presents a method called LEAst-squares Concept Erasure (LEACE) to erase target concept information from every layer in a neural network; it’s used for reducing gender bias in BERT embeddings.                                                                                              | [Paper](https://arxiv.org/abs/2306.03819) , [Tweet](https://twitter.com/norabelrose/status/1666469917636571137?s=20)               |
| 7) **Fine-Grained RLHF** - trains LMs with fine-grained human feedback; instead of using overall preference, more explicit feedback is provided at the segment level which helps to improve efficacy on long-form question answering, reduce toxicity, and enables LM customization.                                           | [Paper](https://arxiv.org/abs/2306.01693), [Tweet](https://twitter.com/zeqiuwu1/status/1665785626552049665?s=20)                   |
| 8) **Hierarchical Vision Transformer** - pretrains vision transformers with a visual pretext task (MAE), while removing unnecessary components from a state-of-the-art multi-stage vision transformer; this enables a simple hierarchical vision transformer that’s more accurate and faster at inference and during training. | [Paper](https://arxiv.org/abs/2306.00989), [Tweet](https://twitter.com/MetaAI/status/1665759715765411840?s=20)                     |
| 9) **Humor in ChatGPT** - explores ChatGPT’s capabilities to grasp and reproduce humor; finds that over 90% of 1008 generated jokes were the same 25 jokes and that ChatGPT is also overfitted to a particular joke structure.                                                                                                 | [Paper](https://arxiv.org/abs/2306.04563), [Tweet](https://twitter.com/AlbertBoyangLi/status/1666707728272850944?s=20)             |
| 10) **Imitating Reasoning Process of Larger LLMs** - develops a 13B parameter model that learns to imitate the reasoning process of large foundational models like GPT-4; it leverages large-scale and diverse imitation data and surpasses instruction-tuned models such as Vicuna-13B in zero-shot reasoning.                | [Paper](https://arxiv.org/abs/2306.02707), [Tweet](https://twitter.com/johnjnay/status/1665906453587034112?s=20)                   |

---

## Top ML Papers of the Week (May 29-June 4)

| **Paper**                                                                                                                                                                                                                                                                                                                      | **Links**                                                                                                                |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------ |
| 1) **Let’s Verify Step by Step** - achieves state-of-the-art mathematical problem solving by rewarding each correct step of reasoning in a chain-of-thought instead of rewarding the final answer; the model solves 78% of problems from a representative subset of the MATH test set.                                         | [Paper](https://arxiv.org/abs/2305.20050), [Tweet](https://twitter.com/OpenAI/status/1663957407184347136?s=20)           |
| 2) **No Positional Encodings** - shows that explicit position embeddings are not essential for decoder-only Transformers; shows that other positional encoding methods like ALiBi and Rotary are not well suited for length generalization.                                                                                    | [Paper](https://arxiv.org/abs/2305.19466), [Tweet](https://twitter.com/a_kazemnejad/status/1664277559968927744?s=20)     |
| 3) **BiomedGPT** - a unified biomedical generative pretrained transformer model for vision, language, and multimodal tasks. Achieves state-of-the-art performance across 5 distinct tasks with 20 public datasets spanning over 15 unique biomedical modalities.                                                               | [Paper](https://arxiv.org/abs/2305.17100), [Tweet](https://twitter.com/omarsar0/status/1662992484576681986?s=20)         |
| 4) **Thought Cloning** - introduces an imitation learning framework to learn to think while acting; the idea is not only to clone the behaviors of human demonstrators but also the thoughts humans have when performing behaviors.                                                                                            | [Paper](https://arxiv.org/abs/2306.00323), [Tweet](https://twitter.com/johnjnay/status/1664798780644904960?s=20)         |
| 5) **Fine-Tuning Language Models with Just Forward Passes** - proposes a memory-efficient zeroth-order optimizer and a corresponding SGD algorithm to finetune large LMs with the same memory footprint as inference.                                                                                                          | [Paper](https://arxiv.org/abs/2305.17333) , [Tweet](https://twitter.com/arankomatsuzaki/status/1663360307274690560?s=20) |
| 6) **MERT** - an acoustic music understanding model with large-scale self-supervised training; it incorporates a superior combination of teacher models to outperform conventional speech and audio approaches.                                                                                                                | [Paper](https://arxiv.org/abs/2306.00107) , [Tweet](https://twitter.com/yizhilll/status/1664680921146982401?s=20)        |
| 7) **Bytes Are All You Need** - investigates performing classification directly on file bytes, without needing to decode files at inference time; achieves ImageNet Top-1 accuracy of 77.33% using a transformer backbone; achieves 95.42% accuracy when operating on WAV files from the Speech Commands v2 dataset.           | [Paper](https://arxiv.org/abs/2306.00238), [Tweet](https://twitter.com/_akhaliq/status/1664497650702471169?s=20)         |
| 8) **Direct Preference Optimization** - while helpful to train safe and useful LLMs, the RLHF process can be complex and often unstable; this work proposes an approach to finetune LMs by solving a classification problem on the human preferences data, with no RL required.                                                | [Paper](https://arxiv.org/abs/2305.18290), [Tweet](https://twitter.com/archit_sharma97/status/1663595372269408261?s=20)  |
| 9) **SQL-PaLM** - an LLM-based Text-to-SQL adopted from PaLM-2; achieves SoTA in both in-context learning and fine-tuning settings; the few-shot model outperforms the previous fine-tuned SoTA by 3.8% on the Spider benchmark; few-shot SQL-PaLM also outperforms few-shot GPT-4 by 9.9%, using a simple prompting approach. | [Paper](https://arxiv.org/abs/2306.00739), [Tweet](https://twitter.com/omarsar0/status/1664441085693657088?s=20)         |
| 10) **CodeTF** - an open-source Transformer library for state-of-the-art code LLMs; supports pretrained code LLMs and popular code benchmarks, including standard methods to train and serve code LLMs efficiently.                                                                                                            | [Paper](https://arxiv.org/abs/2306.00029), [Tweet](https://twitter.com/stevenhoi/status/1664483010954272770?s=20)        |

---

## Top ML Papers of the Week (May 22-28)

| **Paper**                                                                                                                                                                                                                                                                                                       | **Links**                                                                                                                |
| --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------ |
| 1) **QLoRA** - an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning performance.                                                                                                                    | [Paper](https://arxiv.org/abs/2305.14314), [Tweet](https://twitter.com/Tim_Dettmers/status/1661379354507476994?s=20)     |
| 2) **LIMA** - a new 65B parameter LLaMa model fine-tuned on 1000 carefully curated prompts and responses; it doesn't use RLHF, generalizes well to unseen tasks not available in the training data, and generates responses equivalent or preferred to GPT-4 in 43% of cases, and even higher compared to Bard. | [Paper](https://arxiv.org/abs/2305.11206), [Tweet](https://twitter.com/violet_zct/status/1660789120069926912?s=20)       |
| 3) **Voyager** - an LLM-powered embodied lifelong learning agent in Minecraft that can continuously explore worlds, acquire skills, and make novel discoveries without human intervention.                                                                                                                      | [Paper](https://arxiv.org/abs/2305.16291), [Tweet](https://twitter.com/DrJimFan/status/1662115266933972993?s=20)         |
| 4) **Gorilla** - a finetuned LLaMA-based model that surpasses GPT-4 on writing API calls. This capability can help identify the right API, boosting the ability of LLMs to interact with external tools to complete specific tasks.                                                                             | [Paper](https://arxiv.org/abs/2305.15334), [Tweet](https://twitter.com/omarsar0/status/1661540207206846464?s=20)         |
| 5) **The False Promise of Imitating Proprietary LLMs** - provides a critical analysis of models that are finetuned on the outputs of a stronger model; argues that model imitation is a false premise and that the higher leverage action to improve open source models is to develop better base models.       | [Paper](https://arxiv.org/abs/2305.15717) , [Tweet](https://twitter.com/arankomatsuzaki/status/1661908342829187072?s=20) |
| 6) **Sophia** - presents a simple scalable second-order optimizer that has negligible average per-step time and memory overhead; on language modeling, Sophia achieves 2x speed-up compared to Adam in the number of steps, total compute, and wall-clock time.                                                 | [Paper](https://arxiv.org/abs/2305.14342) , [Tweet](https://twitter.com/tengyuma/status/1661412995430219786?s=20)        |
| 7) **The Larger They Are, the Harder They Fail** - shows that LLMs fail to generate correct Python code when default function names are swapped; they also strongly prefer incorrect continuation as they become bigger.                                                                                        | [Paper](https://arxiv.org/abs/2305.15507), [Tweet](https://twitter.com/AVMiceliBarone/status/1662150656327663617?s=20)   |
| 8) **Model Evaluation for Extreme Risks** - discusses the importance of model evaluation for addressing extreme risks and making responsible decisions about model training, deployment, and security.                                                                                                          | [Paper](https://arxiv.org/abs/2305.15324), [Tweet](https://twitter.com/soundboy/status/1661728733156503555?s=20)         |
| 9) **LLM Research Directions** - discusses a list of research directions for students looking to do research with LLMs.                                                                                                                                                                                         | [Paper](https://arxiv.org/abs/2305.12544), [Tweet](https://twitter.com/omarsar0/status/1661405738059571201?s=20)         |
| 10) **Reinventing RNNs for the Transformer Era** - proposes an approach that combines the efficient parallelizable training of Transformers with the efficient inference of RNNs; results show that the method performs on part with similarly sized Transformers.                                              | [Paper](https://arxiv.org/abs/2305.13048), [Tweet](https://twitter.com/_akhaliq/status/1660816265454419969?s=20)         |

---

## Top ML Papers of the Week (May 15-21)

| **Paper**                                                                                                                                                                                                                                                                                                                                                                                                                                                 | **Links**                                                                                                         |
| --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- |
| 1) **Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold** - an approach for controlling GANs that allows dragging points of the image to precisely reach target points in a user-interactive manner.                                                                                                                                                                                                                    | [Paper](https://arxiv.org/abs/2305.10973v1), [Tweet](https://twitter.com/dair_ai/status/1660268470057967616?s=20) |
| 2) **Evidence of Meaning in Language Models Trained on Programs** - argues that language models can learn meaning despite being trained only to perform next token prediction on text.                                                                                                                                                                                                                                                                    | [Paper](https://arxiv.org/abs/2305.11169), [Tweet](https://twitter.com/dair_ai/status/1660268472129945600?s=20)   |
| 3) **Towards Expert-Level Medical Question Answering with Large Language Models** - a top-performing LLM for medical question answering; scored up to 86.5% on the MedQA dataset (a new state-of-the-art); approaches or exceeds SoTA across MedMCQA, PubMedQA, and MMLU clinical topics datasets.                                                                                                                                                        | [Paper](https://arxiv.org/abs/2305.09617), [Tweet](https://twitter.com/dair_ai/status/1660268473853829121?s=20)   |
| 4) **MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers** - a multi-scale decoder architecture enabling end-to-end modeling of sequences of over one million bytes; enables sub-quadratic self-attention and improved parallelism during decoding.                                                                                                                                                                                  | [Paper](https://arxiv.org/abs/2305.07185), [Tweet](https://twitter.com/dair_ai/status/1660268475762327552?s=20)   |
| 5) **StructGPT: A General Framework for Large Language Model to Reason over Structured Data** - improves the zero-shot reasoning ability of LLMs over structured data; effective for solving question answering tasks based on structured data.                                                                                                                                                                                                           | [Paper](https://arxiv.org/abs/2305.09645) , [Tweet](https://twitter.com/dair_ai/status/1660268477628727298?s=20)  |
| 6) **TinyStories: How Small Can Language Models Be and Still Speak Coherent English?** - uses a synthetic dataset of short stories to train and evaluate LMs that are much smaller than SoTA models but can produce fluent and consistent stories with several paragraphs, and demonstrate reasoning capabilities.                                                                                                                                        | [Paper](https://arxiv.org/abs/2305.07759) , [Tweet](https://twitter.com/dair_ai/status/1660268479642054660?s=20)  |
| 7) **DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining** - trains a small proxy model over domains to produce domain weights without knowledge of downstream tasks; it then resamples a dataset with the domain weights and trains a larger model; this enables using a 280M proxy model to train an 8B model (30x larger) more efficiently.                                                                                          | [Paper](https://arxiv.org/abs/2305.10429), [Tweet](https://twitter.com/dair_ai/status/1660268481466572802?s=20)   |
| 8) **CodeT5+: Open Code Large Language Models for Code Understanding and Generation** - supports a wide range of code understanding and generation tasks and different training methods to improve efficacy and computing efficiency; tested on 20 code-related benchmarks using different settings like zero-shot, fine-tuning, and instruction tuning; achieves SoTA on tasks like code completion, math programming, and text-to-code retrieval tasks. | [Paper](https://arxiv.org/abs/2305.07922), [Tweet](https://twitter.com/dair_ai/status/1660268483152584704?s=20)   |
| 9) **Symbol tuning improves in-context learning in language models** - an approach to finetune LMs on in-context input-label pairs where natural language labels are replaced by arbitrary symbols; boosts performance on unseen in-context learning tasks and algorithmic reasoning tasks.                                                                                                                                                               | [Paper](https://arxiv.org/abs/2305.08298)), [Tweet](https://twitter.com/dair_ai/status/1660268485035819009?s=20)  |
| 10) **Searching for Needles in a Haystack: On the Role of Incidental Bilingualism in PaLM's Translation Capability** - shows that PaLM is exposed to over 30 million translation pairs across at least 44 languages; shows that incidental bilingualism connects to the translation capabilities of PaLM.                                                                                                                                                 | [Paper](https://arxiv.org/abs/2305.10266), [Tweet](https://twitter.com/dair_ai/status/1660268486839476224?s=20)   |

---

## Top ML Papers of the Week (May 8-14)

| **Paper**                                                                                                                                                                                                                                                                                                               | **Links**                                                                                                                                                  |
| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1) **LLM explains neurons in LLMs** - applies GPT-4 to automatically write explanations on the behavior of neurons in LLMs and even score those explanations; this offers a promising way to improve interpretability in future LLMs and potentially detect alignment and safety problems.                              | [Paper](https://openaipublic.blob.core.windows.net/neuron-explainer/paper/index.html), [Tweet](https://twitter.com/OpenAI/status/1655982364273831936?s=20) |
| 2) **PaLM 2** - a new state-of-the-art language model integrated into AI features and tools like Bard and the PaLM API; displays competitive performance in mathematical reasoning compared to GPT-4; instruction-tuned model, Flan-PaLM 2, shows good performance on benchmarks like MMLU and BIG-bench Hard.          | [Paper](https://ai.google/static/documents/palm2techreport.pdf), [Tweet](https://twitter.com/Google/status/1656347171556294669?s=20)                       |
| 3) **ImageBind** - an approach that learns joint embedding data across six modalities at once; extends zero-shot capabilities to new modalities and enables emergent applications including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection, and generation.                         | [Paper](https://arxiv.org/abs/2305.05665), [Tweet](https://twitter.com/MetaAI/status/1655989274620358656?s=20)                                             |
| 4) **TidyBot** - shows that robots can combine language-based planning and perception with the few-shot summarization capabilities of LLMs to infer generalized user preferences that are applicable to future interactions.                                                                                            | [Paper](https://arxiv.org/abs/2305.05658), [Tweet](https://twitter.com/_akhaliq/status/1656117478760796160?s=20)                                           |
| 5) **Unfaithful Explanations in Chain-of-Thought Prompting** - demonstrates that CoT explanations can misrepresent the true reason for a model’s prediction; when models are biased towards incorrect answers, CoT generation explanations supporting those answers.                                                    | [Paper](https://arxiv.org/abs/2305.04388) , [Tweet](https://twitter.com/milesaturpin/status/1656010877269602304?s=20)                                      |
| 6) **InstructBLIP** - explores visual-language instruction tuning based on the pre-trained BLIP-2 models; achieves state-of-the-art zero-shot performance on 13 held-out datasets, outperforming BLIP-2 and Flamingo.                                                                                                   | [Paper](https://arxiv.org/abs/2305.06500) , [Tweet](https://twitter.com/LiJunnan0409/status/1656821806593101827?s=20)                                      |
| 7) **Active Retrieval Augmented LLMs** - introduces FLARE, retrieval augmented generation to improve the reliability of LLMs; FLARE actively decides when and what to retrieve across the course of the generation; demonstrates superior or competitive performance on long-form knowledge-intensive generation tasks. | [Paper](https://arxiv.org/abs/2305.06983), [Tweet](https://twitter.com/omarsar0/status/1657004417726423042?s=20)                                           |
| 8) **FrugalGPT** - presents strategies to reduce the inference cost associated with using LLMs while improving performance.                                                                                                                                                                                             | [Paper](https://arxiv.org/abs/2305.05176), [Tweet](https://twitter.com/omarsar0/status/1656105704808419329?s=20)                                           |
| 9) **StarCoder** - an open-access 15.5B parameter LLM with 8K context length and is trained on large amounts of code spanning 80+ programming languages.                                                                                                                                                                | [Paper](https://arxiv.org/abs/2305.06161), [Tweet](https://twitter.com/_akhaliq/status/1656479380296613894?s=20)                                           |
| 10) **MultiModal-GPT** - a vision and language model for multi-round dialogue with humans; the model is fine-tuned from OpenFlamingo, with LoRA added in the cross-attention and self-attention parts of the language model.                                                                                            | [Paper](https://arxiv.org/abs/2305.04790), [Tweet](https://twitter.com/OpenMMLab/status/1656127026687000578?s=20)                                          |

---

## Top ML Papers of the Week (May 1-7)

| **Paper**                                                                                                                                                                                                                                                                                                                                                                                                                       | **Links**                                                                                                                                  |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| 1) **scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI** - a foundation large language model pretrained on 10 million cells for single-cell biology.                                                                                                                                                                                                                                   | [Paper](https://www.biorxiv.org/content/10.1101/2023.04.30.538439v1), [Tweet](https://twitter.com/dair_ai/status/1655223088152211456?s=20) |
| 2) **GPTutor: a ChatGPT-powered programming tool for code explanation** - a ChatGPT-powered tool for code explanation provided as a VSCode extension; claims to deliver more concise and accurate explanations than vanilla ChatGPT and Copilot; performance and personalization enhanced via prompt engineering; programmed to use more relevant code in its prompts.                                                          | [Paper](https://arxiv.org/abs/2305.01863), [Tweet](https://twitter.com/dair_ai/status/1655223089754517509?s=20)                            |
| 3) **Shap-E: Generating Conditional 3D Implicit Functions** - a conditional generative model for 3D assets; unlike previous 3D generative models, this model generates implicit functions that enable rendering textured meshes and neural radiance fields.                                                                                                                                                                     | [Paper](https://arxiv.org/abs/2305.02463), [Tweet](https://twitter.com/dair_ai/status/1655223091482566663?s=20)                            |
| 4) **Are Emergent Abilities of Large Language Models a Mirage?** - presents an alternative explanation to the emergent abilities of LLMs; suggests that existing claims are creations of the researcher’s analyses and not fundamental changes in model behavior on specific tasks with scale                                                                                                                                   | [Paper](https://arxiv.org/abs/2304.15004), [Tweet](https://twitter.com/dair_ai/status/1655223092975640578?s=20)                            |
| 5) **Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl** - releases PySR, an open-source library for practical symbolic regression for the sciences; it’s built on a high-performance distributed back-end and interfaces with several deep learning packages; in addition, a new benchmark, “EmpiricalBench”, is released to quantify applicability of symbolic regression algorithms in science. | [Paper](https://arxiv.org/abs/2305.01582) , [Tweet](https://twitter.com/dair_ai/status/1655223094640889856?s=20)                           |
| 6) **PMC-LLaMA: Further Finetuning LLaMA on Medical Papers** - a LLaMA model fine-tuned on 4.8 million medical papers; enhances capabilities in the medical domain and achieves high performance on biomedical QA benchmarks.                                                                                                                                                                                                   | [Paper](https://arxiv.org/abs/2304.14454) , [Tweet](https://twitter.com/dair_ai/status/1655223096301740032?s=20)                           |
| 7) **Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes** - a mechanism to extract rationales from LLMs to train smaller models that outperform larger language models with less training data needed by finetuning or distillation.                                                                                                                                 | [Paper](https://arxiv.org/abs/2305.02301), [Tweet](https://twitter.com/dair_ai/status/1655223098730217472?s=20)                            |
| 8) **Poisoning Language Models During Instruction Tuning** - show that adversaries can poison LLMs during instruction tuning by contributing poison examples to datasets; it can induce degenerate outputs across different held-out tasks.                                                                                                                                                                                     | [Paper](https://arxiv.org/abs/2305.00944), [Tweet](https://twitter.com/dair_ai/status/1655223100286332934?s=20)                            |
| 9) **Unlimiformer: Long-Range Transformers with Unlimited Length Input** - proposes long-range transformers with unlimited length input by augmenting pre-trained encoder-decoder transformer with external datastore to support unlimited length input; shows usefulness in long-document summarization; could potentially be used to improve the performance of retrieval-enhanced LLMs.                                      | [Paper](https://arxiv.org/abs/2305.01625), [Tweet](https://twitter.com/dair_ai/status/1655223101913718784?s=20)                            |
| 10) **Learning to Reason and Memorize with Self-Notes** - an approach that enables LLMs to reason and memorize enabling them to deviate from the input sequence at any time to explicitly “think”; this enables the LM to recall information and perform reasoning on the fly; experiments show that this method scales better to longer sequences unseen during training.                                                      | [Paper](https://arxiv.org/abs/2305.00833), [Tweet](https://twitter.com/dair_ai/status/1655223103662829569?s=20)                            |

---

## Top ML Papers of the Week (April 24 - April 30)

| **Paper**                                                                                                                                                                                                                                                                                                           | **Links**                                                                                                                                                |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1) **Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning** - applies deep reinforcement learning to synthesize agile soccer skills for a miniature humanoid robot; the resulting policy allows dynamic movement skills such as fast recovery, walking, and kicking.                   | [Paper](https://arxiv.org/abs/2304.13653), [Tweet](https://twitter.com/dair_ai/status/1652693172810571780?s=20)                                          |
| 2) **Scaling Transformer to 1M tokens and beyond with RMT** - leverages a recurrent memory transformer architecture to increase BERT’s effective context length to two million tokens while maintaining high memory retrieval accuracy.                                                                             | [Paper](https://arxiv.org/abs/2304.11062), [Tweet](https://twitter.com/dair_ai/status/1652693174576349185?s=20)                                          |
| 3) **Track Anything: Segment Anything Meets Videos** - an interactive tool for video object tracking and segmentation; it’s built on top segment anything and allows flexible tracking and segmenting via user clicks.                                                                                              | [Paper](https://arxiv.org/abs/2304.11968), [Tweet](https://twitter.com/dair_ai/status/1652693176644165634?s=20)                                          |
| 4) **A Cookbook of Self-Supervised Learning** - provides an overview of fundamental techniques and key concepts in SSL; it also introduces practical considerations for implementing SSL methods successfully.                                                                                                      | [Paper](https://arxiv.org/abs/2304.12210), [Tweet](https://twitter.com/dair_ai/status/1652693178724626435?s=20)                                          |
| 5) **Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond** - a comprehensive and practical guide for practitioners working with LLMs; discusses many use cases with practical applications and limitations of LLMs in real-world scenarios.                                                    | [Paper](https://arxiv.org/abs/2304.13712) , [Tweet](https://twitter.com/dair_ai/status/1652693180381274114?s=20)                                         |
| 6) **AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head** - connects ChatGPT with audio foundational models to handle challenging audio tasks and a modality transformation interface to enable spoken dialogue.                                                                         | [Paper](https://arxiv.org/abs/2304.12995) , [Tweet](https://twitter.com/dair_ai/status/1652693181895409666?s=20)                                         |
| 7) **DataComp: In search of the next generation of multimodal datasets** - releases a new multimodal dataset benchmark containing 12.8B image-text pairs.                                                                                                                                                           | [Paper](https://arxiv.org/abs/2304.14108), [Tweet](https://twitter.com/dair_ai/status/1652693183493447681?s=20)                                          |
| 8) **ChatGPT for Information Extraction** - provides a deeper assessment of ChatGPT's performance on the important information extraction task.                                                                                                                                                                     | [Paper](https://arxiv.org/abs/2304.11633), [Tweet](https://twitter.com/dair_ai/status/1652693184927989768?s=20)                                          |
| 9) **Comparing Physician vs ChatGPT** - investigates if chatbot assistants like ChatGPT can provide responses to patient questions while emphasizing quality and empathy; finds that chatbot responses were preferred over physician responses and rated significantly higher in terms of both quality and empathy. | [Paper](https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2804309), [Tweet](https://twitter.com/dair_ai/status/1652693186467299331?s=20) |
| 10) **Stable and low-precision training for large-scale vision-language models** - introduces methods for accelerating and stabilizing training of large-scale language vision models.                                                                                                                              | [Paper](https://arxiv.org/abs/2304.13013), [Tweet](https://twitter.com/dair_ai/status/1652693187960479745?s=20)                                          |

---

## Top ML Papers of the Week (April 17 - April 23)

| **Paper**                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 | **Links**                                                                                                        |
|   | ---------------------------------------------------------------------------------------------------------------- |
| 1) **DINOv2: Learning Robust Visual Features without Supervision** - a new method for training high-performance computer vision models based on self-supervised learning; enables learning rich and robust visual features without supervision which are useful for both image-level visual tasks and pixel-level tasks; tasks supported include image classification, instance retrieval, video understanding, depth estimation, and much more.                                                                                          | [Paper](https://arxiv.org/abs/2304.07193), [Tweet](https://twitter.com/dair_ai/status/1650145892941324288?s=20)  |
| 2) **Learning to Compress Prompts with Gist Tokens** - an approach that trains language models to compress prompts into gist tokens reused for compute efficiency; this approach enables 26x compression of prompts, resulting in up to 40% FLOPs reductions.                                                                                                                                                                                                                                                                             | [Paper](https://arxiv.org/abs/2304.08467), [Tweet](https://twitter.com/dair_ai/status/1650145895332163585?s=20)  |
| 3) **Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size** - presents a framework for large-scale biomolecular simulation; this is achieved through the high accuracy of equivariant deep learning and the ability to scale to large and long simulations; the system is able to “perform nanoseconds-long stable simulations of protein dynamics and scale up to a 44-million atom structure of a complete, all-atom, explicitly solvated HIV capsid on the Perlmutter supercomputer.” | [Paper](https://arxiv.org/abs/2304.10061), [Tweet](https://twitter.com/dair_ai/status/1650145897689350144?s=20)  |
| 4) **Evaluating Verifiability in Generative Search Engines** - performs human evaluation to audit popular generative search engines such as Bing Chat, Perplexity AI, and NeevaAI; finds that, on average, only 52% of generated sentences are supported by citations and 75% of citations support their associated sentence.                                                                                                                                                                                                             | [Paper](https://arxiv.org/abs/2304.09848), [Tweet](https://twitter.com/dair_ai/status/1650145900180779009?s=20)  |
| 5) **Generative Disco: Text-to-Video Generation for Music Visualization** - an AI system based on LLMs and text-to-image models that generates music visualizations.                                                                                                                                                                                                                                                                                                                                                                      | [Paper](https://arxiv.org/abs/2304.08551) , [Tweet](https://twitter.com/dair_ai/status/1650145904219832324?s=20) |
| 6) **Architectures of Topological Deep Learning: A Survey on Topological Neural Networks**                                                                                                                                                                                                                                                                                                                                                                                                                                                | [Paper](https://arxiv.org/abs/2304.10031) , [Tweet](https://twitter.com/dair_ai/status/1650145906560311298?s=20) |
| 7) **Visual Instruction Tuning** - presents an approach that uses language-only GPT-4 to generate multimodal language-image instruction-following data; applies instruction tuning with the data and introduces LLaVA, an end-to-end trained large multimodal model for general-purpose visual and language understanding.                                                                                                                                                                                                                | [Paper](https://arxiv.org/abs/2304.08485), [Tweet](https://twitter.com/dair_ai/status/1650145909387214848?s=20)  |
| 8) **ChatGPT: Applications, Opportunities, and Threats**                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  | [Paper](https://arxiv.org/abs/2304.09103), [Tweet](https://twitter.com/dair_ai/status/1650145911836745736?s=20)  |
| 9) **Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models** - a plug-and-play compositional reasoning framework that augments LLMs and can infer the appropriate sequence of tools to compose and execute in order to generate final responses; achieves 87% accuracy on ScienceQA and 99% on TabMWP.                                                                                                                                                                                                              | [Paper](https://arxiv.org/abs/2304.09842), [Tweet](https://twitter.com/dair_ai/status/1650145914420330496?s=20)  |
| 10) **Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models** - applies latent diffusion models to high-resolution video generation; validates the model on creative content creation and real driving videos of 512 x 1024 and achieves state-of-the-art performance.                                                                                                                                                                                                                                         | [Paper](https://arxiv.org/abs/2304.08818), [Tweet](https://twitter.com/dair_ai/status/1650145916794314752?s=20)  |

---

## Top ML Papers of the Week (April 10 - April 16)

| **Paper**                                                                                                                                                                                                                                                                                                                                                                               | **Links**                                                                                                        |
| --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------- |
| 1) **Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields** - combines mip-NeRF 360 and grid-based models to improve NeRFs that train 22x faster than mip-NeRF 360.                                                                                                                                                                                                                 | [Paper](https://arxiv.org/abs/2304.06706), [Tweet](https://twitter.com/dair_ai/status/1647613826425147401?s=20)  |
| 2) **Generative Agents: Interactive Simulacra of Human Behavior** - proposes an architecture that extends LLMs to build agents that enable simulations of human-like behavior; these capabilities are possible by storing a complete record of an agent's experiences, synthesizing memories over time into higher-level reflections, and retrieving them dynamically to plan behavior. | [Paper](https://arxiv.org/abs/2304.03442), [Tweet](https://twitter.com/dair_ai/status/1647613828417351682?s=20)  |
| 3) **Emergent autonomous scientific research capabilities of large language models** - presents an agent that combines LLMs for autonomous design, planning, and execution of scientific experiments; shows emergent scientific research capabilities, including the successful performance of catalyzed cross-coupling reactions.                                                      | [Paper](https://arxiv.org/abs/2304.05332), [Tweet](https://twitter.com/dair_ai/status/1647613830233571328?s=20)  |
| 4) **Automatic Gradient Descent: Deep Learning without Hyperparameters** - derives optimization algorithms that explicitly leverage neural architecture; it proposes a first-order optimizer without hyperparameters that trains CNNs at ImageNet scale.                                                                                                                                | [Paper](https://arxiv.org/abs/2304.05187), [Tweet](https://twitter.com/dair_ai/status/1647613832804589569?s=20)  |
| 5) **ChemCrow: Augmenting large-language models with chemistry tools** - presents an LLM chemistry agent that performs tasks across synthesis, drug discovery, and materials design; it integrates 13 expert-design tools to augment LLM performance in chemistry and demonstrate effectiveness in automating chemical tasks.                                                           | [Paper](https://arxiv.org/abs/2304.05376) , [Tweet](https://twitter.com/dair_ai/status/1647613834813644800?s=20) |
| 6) **One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era** - A Survey of ChatGPT and GPT-4                                                                                                                                                                                                                                               | [Paper](https://arxiv.org/abs/2304.06488) , [Tweet](https://twitter.com/dair_ai/status/1647613836617195525?s=20) |
| 7) **OpenAGI: When LLM Meets Domain Experts** - an open-source research platform to facilitate the development and evaluation of LLMs in solving complex, multi-step tasks through manipulating various domain expert models.                                                                                                                                                           | [Paper](https://arxiv.org/abs/2304.04370), [Tweet](https://twitter.com/dair_ai/status/1647613838567546886?s=20)  |
| 8) **AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models** - a new benchmark to assess foundational models in the context of human-centric standardized exams, including college entrance exams, law school admission tests, and math competitions, among others.                                                                                                       | [Paper](https://arxiv.org/abs/2304.06364), [Tweet](https://twitter.com/dair_ai/status/1647613840400498700?s=20)  |
| 9) **Teaching Large Language Models to Self-Debug** - proposes an approach that teaches LLMs to debug their predicted program via few-shot demonstrations; this allows a model to identify its mistakes by explaining generated code in natural language; achieves SoTA on several code generation tasks like text-to-SQL generation.                                                   | [Paper](https://arxiv.org/abs/2304.05128), [Tweet](https://twitter.com/dair_ai/status/1647613842300497924?s=20)  |
| 10) **Segment Everything Everywhere All at Once** - a promptable, interactive model for various segmentation tasks that yields competitive performance on open-vocabulary and interactive segmentation benchmarks.                                                                                                                                                                      | [Paper](https://arxiv.org/abs/2304.06718), [Tweet](https://twitter.com/dair_ai/status/1647613844087361537?s=20)  |

## Top ML Papers of the Week (April 3 - April 9)

| **Paper**                                                                                                                                                                                                                                                                                                                                                              | **Links**                                                                                                         |
| ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- |
| 1) **Segment Anything** - presents a set of resources to establish foundational models for image segmentation; releases the largest segmentation dataset with over 1 billion masks on 11M licensed images; the model’s zero-shot performance is competitive with or even superior to fully supervised results.                                                         | [Paper](https://arxiv.org/abs/2304.02643v1), [Tweet](https://twitter.com/dair_ai/status/1645089444280561666?s=20) |
| 2) **Instruction Tuning with GPT-4** - presents GPT-4-LLM, a "first attempt" to use GPT-4 to generate instruction-following data for LLM fine-tuning; the dataset is released and includes 52K unique English and Chinese instruction-following data; the dataset is used to instruction-tune LLaMA models which leads to superior zero-shot performance on new tasks. | [Paper](https://arxiv.org/abs/2304.03277), [Tweet](https://twitter.com/dair_ai/status/1645089446524534788?s=20)   |
| 3) **Eight Things to Know about Large Language Models** - discusses important considerations regarding the capabilities and limitations of LLMs.                                                                                                                                                                                                                       | [Paper](https://arxiv.org/abs/2304.00612v1), [Tweet](https://twitter.com/dair_ai/status/1645089448428699650?s=20) |
| 4) **A Survey of Large Language Models** - a new 50 pages survey on large language models.                                                                                                                                                                                                                                                                             | [Paper](https://arxiv.org/abs/2303.18223), [Tweet](https://twitter.com/dair_ai/status/1645089450395852802?s=20)   |
| 5) **Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data** - an open-source chat model fine-tuned with LoRA. Leverages 100K dialogs generated from ChatGPT chatting with itself; it releases the dialogs along with 7B, 13B, and 30B parameter models.                                                                                  | [Paper](https://arxiv.org/abs/2304.01196) , [Tweet](https://twitter.com/dair_ai/status/1645089452081938433?s=20)  |
| 6) **Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark** - a new benchmark of 134 text-based Choose-Your-Own-Adventure games to evaluate the capabilities and unethical behaviors of LLMs.                                                                                                      | [Paper](https://arxiv.org/abs/2304.03279) , [Tweet](https://twitter.com/dair_ai/status/1645089453780639744?s=20)  |
| 7) **Better Language Models of Code through Self-Improvement** - generates pseudo data from knowledge gained through pre-training and fine-tuning; adds the data to the training dataset for the next step; results show that different frameworks can be improved in performance using code-related generation tasks.                                                 | [Paper](https://arxiv.org/abs/2304.01228v1), [Tweet](https://twitter.com/dair_ai/status/1645089455659687937?s=20) |
| 8) **Summary of ChatGPT/GPT-4 Research and Perspective Towards the Future of Large Language Models** - an overview of applications of ChatGPT and GPT-4; the analysis is done on 194 relevant papers and discusses capabilities, limitations, concerns, and more                                                                                                       | [Paper](https://arxiv.org/abs/2304.01852), [Tweet](https://twitter.com/dair_ai/status/1645089457488404486?s=20)   |
| 9) **Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling** - a suite for analyzing LLMs across training and scaling; includes 16 LLMs trained on public data and ranging in size from 70M to 12B parameters.                                                                                                                               | [Paper](https://arxiv.org/abs/2304.01373), [Tweet](https://twitter.com/dair_ai/status/1645089459191382016?s=20)   |
| 10) **SegGPT: Segmenting Everything In Context** - unifies segmentation tasks into a generalist model through an in-context framework that supports different kinds of data.                                                                                                                                                                                           | [Paper](https://arxiv.org/abs/2304.03284), [Tweet](https://twitter.com/dair_ai/status/1645089461124886529?s=20)   |

---

## Top ML Papers of the Week (Mar 27 - April 2)

| **Paper**                                                                                                                                                                                                                                                                                                                                                                      | **Links**                                                                                                                |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------ |
| 1) **BloombergGPT: A Large Language Model for Finance** - a new 50B parameter large language model for finance. Claims the largest domain-specific dataset yet with 363 billion tokens... further augmented with 345 billion tokens from general-purpose datasets; outperforms existing models on financial tasks while not sacrificing performance on general LLM benchmarks. | [Paper](https://arxiv.org/abs/2303.17564v1), [Tweet](https://twitter.com/omarsar0/status/1641787456436547584?s=20)       |
| 2) **Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware** - a low-cost system that performs end-to-end imitation learning from real demonstrations; also presents an algorithm called Action Chunking with Transformers to learn a generative model that allows a robot to learn difficult tasks in the real world.                                            | [Paper](https://tonyzhaozh.github.io/aloha/), [Tweet](https://twitter.com/tonyzzhao/status/1640393026341322754?s=20)     |
| 3) **HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace** - a system that leverages LLMs like ChatGPT to conduct task planning, select models and act as a controller to execute subtasks and summarize responses according to execution results.                                                                                                        | [Paper](https://arxiv.org/abs/2303.17580), [Tweet](https://twitter.com/johnjnay/status/1641609645713129473?s=20)         |
| 4) **ChatDoctor: A Medical Chat Model Fine-tuned on LLaMA Model using Medical Domain Knowledge** - a medical chat model fine-tuned on LLaMA using medical domain knowledge. Collects data on around 700 diseases and generated 5K doctor-patient conversations to finetune the LLM.                                                                                            | [Paper](https://arxiv.org/abs/2303.14070), [Tweet](https://twitter.com/omarsar0/status/1640525256719753217?s=20)         |
| 5) **LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention** - a lightweight adaption method to efficiently fine-tune LLaMA into an instruction-following model; generates responses comparable to Alpaca with fully fine-tuned 7B parameter; it’s also extended for multi-modal input support.                                                     | [Paper](https://arxiv.org/abs/2303.16199) , [Tweet](https://twitter.com/rasbt/status/1641457696074334209?s=20)           |
| 6) **ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks** - demonstrates that ChatGPT can outperform crowd-workers for several annotation tasks such as relevance, topics, and frames detection; besides better zero-shot accuracy, the per-annotation cost of ChatGPT is less 20 times cheaper than MTurk.                                                           | [Paper](https://arxiv.org/abs/2303.15056v1) , [Tweet](https://twitter.com/AlphaSignalAI/status/1641496876527517696?s=20) |
| 7) **Language Models can Solve Computer Tasks** - shows that a pre-trained LLM agent can execute computer tasks using a simple prompting scheme where the agent recursively criticizes and improves its outputs.                                                                                                                                                               | [Paper](https://arxiv.org/abs/2303.17491), [Tweet](https://twitter.com/arankomatsuzaki/status/1641609722951516161?s=20)  |
| 8) **DERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving Agents** - a paradigm to enhance large language model completions by allowing models to communicate feedback and iteratively improve output; DERA outperforms base GPT-4 on clinically-focused tasks.                                                                                      | [Paper](https://arxiv.org/abs/2303.17071), [Tweet](https://twitter.com/johnjnay/status/1642168727796961280?s=20)         |
| 9) **Natural Selection Favors AIs over Humans** - discusses why AI systems will become more fit than humans and the potential dangers and risks involved, including ways to mitigate them.                                                                                                                                                                                     | [Paper](https://arxiv.org/abs/2303.16200), [Tweet](https://twitter.com/DanHendrycks/status/1641102660412792833?s=20)     |
| 10) **Machine Learning for Partial Differential Equations** - Pa review examining avenues of partial differential equations research advanced by machine learning.                                                                                                                                                                                                             | [Paper](https://arxiv.org/abs/2303.17078), [Tweet](https://twitter.com/DynamicsSIAM/status/1641608068453777412?s=20)     |

---

## Top ML Papers of the Week (Mar 20-Mar 26)

| **Paper**                                                                                                                                                                                                                                                                                                                                    | **Links**                                                                                                                                                                                |
| -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1) **Sparks of Artificial General Intelligence: Early experiments with GPT-4** - a comprehensive investigation of an early version of GPT-4 when it was still in active development by OpenAI.                                                                                                                                               | [Paper](https://arxiv.org/abs/2303.12712), [Tweet](https://twitter.com/dair_ai/status/1639991716349460481?s=20)                                                                          |
| 2) **Reflexion: an autonomous agent with dynamic memory and self-reflection** - proposes an agent with dynamic memory and self-reflection capabilities to enhance its existing reasoning trace and task-specific action choice abilities.                                                                                                    | [Paper](https://arxiv.org/abs/2303.11366), [Tweet](https://twitter.com/dair_ai/status/1639991718169722880?s=20)                                                                          |
| 3) **Capabilities of GPT-4 on Medical Challenge Problems** - shows that GPT-4 exceeds the passing score on USMLE by over 20 points and outperforms GPT-3.5 as well as models specifically fine-tuned on medical knowledge (Med-PaLM, a prompt-tuned version of Flan-PaLM 540B).                                                              | [Paper](https://www.microsoft.com/en-us/research/publication/capabilities-of-gpt-4-on-medical-challenge-problems/), [Tweet](https://twitter.com/dair_ai/status/1639991720224989188?s=20) |
| 4) **GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models** - investigates the potential implications of GPT models and related systems on the US labor market.                                                                                                                                        | [Paper](https://arxiv.org/abs/2303.10130), [Tweet](https://twitter.com/dair_ai/status/1639991722263412737?s=20)                                                                          |
| 5) **CoLT5: Faster Long-Range Transformers with Conditional Computation** - a long-input Transformer model that employs conditional computation, devoting more resources to important tokens in both feedforward and attention layers.                                                                                                       | [Paper](https://arxiv.org/abs/2303.09752) , [Tweet](https://twitter.com/dair_ai/status/1639991723806826499?s=20)                                                                         |
| 6) **Artificial muses: Generative Artificial Intelligence Chatbots Have Risen to Human-Level Creativity** - compares human-generated ideas with those generated by generative AI chatbots like ChatGPT and YouChat; reports that 9.4% of humans were more creative than GPT-4 and that GAIs are valuable assistants in the creative process. | [Paper](https://arxiv.org/abs/2303.12003) , [Tweet](https://twitter.com/dair_ai/status/1639991725442646018?s=20)                                                                         |
| 7) **A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models** - a comprehensive capability analysis of GPT series models; evaluates performance on 9 natural language understanding tasks using 21 datasets.                                                                                                                 | [Paper](https://arxiv.org/abs/2303.10420), [Tweet](https://twitter.com/dair_ai/status/1639991727292395520?s=20)                                                                          |
| 8) **Context-faithful Prompting for Large Language Models** - presents a prompting technique that aims to improve LLMs' faithfulness using strategies such as opinion-based prompts and counterfactual demonstrations.                                                                                                                       | [Paper](https://arxiv.org/abs/2303.11315), [Tweet](https://twitter.com/dair_ai/status/1639991728882032646?s=20)                                                                          |
| 9) **Text2Room: Extracting Textured 3D Meshes from 2D Text-to-Image Models** - a method for extracting room-scale textured 3D meshes from 2D text-to-image models.                                                                                                                                                                           | [Paper](https://arxiv.org/abs/2303.11989), [Project](https://lukashoel.github.io/text-to-room/)[Tweet](https://twitter.com/dair_ai/status/1639991730723254274?s=20)                      |
| 10) **PanGu-Σ: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing** - a trillion parameter language model with sparse heterogeneous computing.                                                                                                                                                                    | [Paper](https://arxiv.org/abs/2303.10845), [Tweet](https://twitter.com/dair_ai/status/1639991732405252100?s=20)                                                                          |

---

## Top ML Papers of the Week (Mar 13-Mar 19)

| **Paper**                                                                                                                                                                                                                                                            | **Links**                                                                                                                                                        |
| -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1) **GPT-4 Technical Report** - GPT-4 - a large multimodal model with broader general knowledge and problem-solving abilities.                                                                                                                                       | [Paper](https://arxiv.org/abs/2303.08774v2), [Tweet](https://twitter.com/dair_ai/status/1637456913993433089?s=20)                                                |
| 2) **LERF: Language Embedded Radiance Fields** - a method for grounding language embeddings from models like CLIP into NeRF; this enables open-ended language queries in 3D.                                                                                         | [Paper](https://arxiv.org/abs/2303.09553), [Tweet](https://twitter.com/dair_ai/status/1637456915658686465?s=20)                                                  |
| 3) **An Overview on Language Models: Recent Developments and Outlook** - an overview of language models covering recent developments and future directions. It also covers topics like linguistic units, structures, training methods, evaluation, and applications. | [Paper](https://arxiv.org/abs/2303.05759), [Tweet](https://twitter.com/omarsar0/status/1635273656858460162?s=20)                                                 |
| 4) **Eliciting Latent Predictions from Transformers with the Tuned Lens** - a method for transformer interpretability that can trace a language model predictions as it develops layer by layer.                                                                     | [Paper](https://arxiv.org/abs/2303.08112), [Tweet](https://twitter.com/dair_ai/status/1637456919819440130?s=20)                                                  |
| 5) **Meet in the Middle: A New Pre-training Paradigm** - a new pre-training paradigm using techniques that jointly improve training data efficiency and capabilities of LMs in the infilling task; performance improvement is shown in code generation tasks.        | [Paper](https://arxiv.org/abs/2303.07295) , [Tweet](https://twitter.com/dair_ai/status/1637456922004561920?s=20)                                                 |
| 6) **Resurrecting Recurrent Neural Networks for Long Sequences** - demonstrates that careful design of deep RNNs using standard signal propagation arguments can recover the performance of deep state-space models on long-range reasoning tasks.                   | [Paper](https://arxiv.org/abs/2303.06349) , [Tweet](https://twitter.com/dair_ai/status/1637456923795521537?s=20)                                                 |
| 7) **UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation** - a new approach to tune a lightweight and versatile retriever to automatically retrieve prompts to improve zero-shot performance and help mitigate hallucinations.                     | [Paper](https://arxiv.org/abs/2303.08518), [Tweet](https://twitter.com/dair_ai/status/1637456925779456000?s=20)                                                  |
| 8) **Patches Are All You Need?** - proposes ConvMixer, a parameter-efficient fully-convolutional model which replaces self-attention and MLP layers in ViTs with less-expressive depthwise and pointwise convolutional layers.                                       | [Paper](https://openreview.net/forum?id=rAnB7JSMXL), [Tweet](https://twitter.com/dair_ai/status/1637456927784329218?s=20)                                        |
| 9) **NeRFMeshing: Distilling Neural Radiance Fields into Geometrically-Accurate 3D Meshes** - a compact and flexible architecture that enables easy 3D surface reconstruction from any NeRF-driven approach; distills NeRFs into geometrically-accurate 3D meshes.   | [Paper](https://arxiv.org/abs/2303.09431), [Tweet](https://twitter.com/dair_ai/status/1637456929705295873?s=20)                                                  |
| 10) **High-throughput Generative Inference of Large Language Models with a Single GPU** - a high-throughput generation engine for running LLMs with limited GPU memory.                                                                                              | [Paper](https://arxiv.org/abs/2303.06865), [Code](https://github.com/FMInference/FlexGen) , [Tweet](https://twitter.com/dair_ai/status/1637456931429183489?s=20) |

---

## Top ML Papers of the Week (Mar 6-Mar 12)

| **Paper**                                                                                                                                                                                                                                                                             | **Links**                                                                                                                                                                                                         |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1) **PaLM-E: An Embodied Multimodal Language Model** - incorporates real-world continuous sensor modalities resulting in an embodied LM that performs tasks such as robotic manipulation planning, visual QA, and other embodied reasoning tasks.                                     | [Paper](https://arxiv.org/abs/2303.03378), [Demo](https://palm-e.github.io/) , [Tweet](https://twitter.com/dair_ai/status/1634919222420836358?s=20)                                                               |
| 2) **Prismer: A Vision-Language Model with An Ensemble of Experts** - a parameter-efficient vision-language model powered by an ensemble of domain experts; it efficiently pools expert knowledge from different domains and adapts it to various vision-language reasoning tasks.    | [Paper](https://arxiv.org/abs/2303.02506), [GitHub](https://github.com/NVlabs/Prismer), [Project](https://shikun.io/projects/prismer) , [Tweet](https://twitter.com/dair_ai/status/1634919224505257985?s=20)      |
| 3) **Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models** - it connects ChatGPT and different visual foundation models to enable users to interact with ChatGPT beyond language format.                                                                       | [Paper](https://arxiv.org/abs/2303.04671), [GitHub](https://github.com/microsoft/visual-chatgpt) [Tweet](https://twitter.com/dair_ai/status/1634919226396794882?s=20)                                             |
| 4) **A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT** - an overview of generative AI - from GAN to ChatGPT.                                                                                                                    | [Paper](https://arxiv.org/abs/2303.04226), [Tweet](https://twitter.com/dair_ai/status/1634919228339003393?s=20)                                                                                                   |
| 5) **Larger language models do in-context learning differently** - shows that with scale, LLMs can override semantic priors when presented with enough flipped labels; these models can also perform well when replacing targets with semantically-unrelated targets.                 | [Paper](https://arxiv.org/abs/2303.03846) , [Tweet](https://twitter.com/dair_ai/status/1634919230461345797?s=20)                                                                                                  |
| 6) **Foundation Models for Decision Making: Problems, Methods, and Opportunities** - provides an overview of foundation models for decision making, including tools, methods, and new research directions.                                                                            | [Project](https://arxiv.org/abs/2303.04129) , [Tweet](https://twitter.com/dair_ai/status/1634919232650760192?s=20)                                                                                                |
| 7) **Hyena Hierarchy: Towards Larger Convolutional Language Models** - a subquadratic drop-in replacement for attention; it interleaves implicit long convolutions and data-controlled gating and can learn on sequences 10x longer and up to 100x faster than optimized attention.   | [Paper](https://arxiv.org/abs/2302.10866), [Code](https://github.com/HazyResearch/safari), [Blog](https://ermongroup.github.io/blog/hyena/), [Tweet](https://twitter.com/dair_ai/status/1634919234835980289?s=20) |
| 8) **OpenICL: An Open-Source Framework for In-context Learning** - a new open-source toolkit for in-context learning and LLM evaluation; supports various state-of-the-art retrieval and inference methods, tasks, and zero-/few-shot evaluation of LLMs.                             | [Paper](https://arxiv.org/abs/2303.02913), [Repo](https://github.com/Shark-NLP/OpenICL), [Tweet](https://twitter.com/dair_ai/status/1634919236954132480?s=20)                                                     |
| 9) **MathPrompter: Mathematical Reasoning using Large Language Models** - a technique that improves LLM performance on mathematical reasoning problems; it uses zero-shot chain-of-thought prompting and verification to ensure generated answers are accurate.                       | [Paper](https://arxiv.org/abs/2303.05398), [Tweet](https://twitter.com/dair_ai/status/1634919239030280197?s=20)                                                                                                   |
| 10) **Scaling up GANs for Text-to-Image Synthesis** - enables scaling up GANs on large datasets for text-to-image synthesis; it’s found to be orders of magnitude faster at inference time, synthesizes high-resolution images, & supports various latent space editing applications. | [Paper](https://arxiv.org/abs/2303.05511), [Project](https://mingukkang.github.io/GigaGAN/) , [Tweet](https://twitter.com/dair_ai/status/1634919241198751744?s=20)                                                |

---

## Top ML Papers of the Week (Feb 27-Mar 5)

| **Paper**                                                                                                                                                                                                                                                                                                                | **Links**                                                                                                                                                                                                                       |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1) **Language Is Not All You Need: Aligning Perception with Language Models** - introduces a multimodal large language model called Kosmos-1; achieves great performance on language understanding, OCR-free NLP, perception-language tasks, visual QA, and more.                                                        | [Paper](https://arxiv.org/abs/2302.14045), [Tweet](https://twitter.com/dair_ai/status/1632383312550416384?s=20)                                                                                                                 |
| 2) **Evidence of a predictive coding hierarchy in the human brain listening to speech** - finds that human brain activity is best explained by the activations of modern language models enhanced with long-range and hierarchical predictions.                                                                          | [Paper](https://www.nature.com/articles/s41562-022-01516-2?utm_source=twitter&utm_medium=organic_social&utm_campaign=evergreen&utm_content=animation), [Tweet](https://twitter.com/dair_ai/status/1632383315029180416?s=20)     |
| 3) **EvoPrompting: Language Models for Code-Level Neural Architecture Search** - combines evolutionary prompt engineering with soft prompt-tuning to find high-performing models; it leverages few-shot prompting which is further improved by using an evolutionary search approach to improve the in-context examples. | [Paper](https://arxiv.org/abs/2302.14838), [Tweet](https://twitter.com/dair_ai/status/1632383317302562816?s=20)                                                                                                                 |
| 4) **Consistency Models** - a new family of generative models that achieve high sample quality without adversarial training.                                                                                                                                                                                             | [Paper](https://arxiv.org/abs/2303.01469), [Tweet](https://twitter.com/dair_ai/status/1632383319152132096?s=20)                                                                                                                 |
| 5) **Goal Driven Discovery of Distributional Differences via Language Descriptions** - a new task that automatically discovers corpus-level differences via language description in a goal-driven way; applications include discovering insights from commercial reviews and error patterns in NLP systems.              | [Paper](https://arxiv.org/abs/2302.14233) , [Code](https://github.com/ruiqi-zhong/D5), [Tweet](https://twitter.com/dair_ai/status/1632383321035374593?s=20)                                                                     |
| 6) **High-resolution image reconstruction with latent diffusion models from human brain activity** - proposes an approach for high-resolution image reconstruction with latent diffusion models from human brain activity.                                                                                               | [Project](https://sites.google.com/view/stablediffusion-with-brain/) , [Tweet](https://twitter.com/dair_ai/status/1632383323086487554?s=20)                                                                                     |
| 7) **Grounded Decoding: Guiding Text Generation with Grounded Models for Robot Control** - a scalable approach to planning with LLMs in embodied settings through grounding functions; GD is found to be a general, flexible, and expressive approach to embodied tasks.                                                 | [Paper](https://grounded-decoding.github.io/paper.pdf), [Project](https://grounded-decoding.github.io/) [Tweet](https://twitter.com/dair_ai/status/1632383325036740610?s=20)                                                    |
| 8) **Language-Driven Representation Learning for Robotics** - a framework for language-driven representation learning from human videos and captions for robotics.                                                                                                                                                       | [Paper](https://arxiv.org/abs/2302.12766), [Models](https://github.com/siddk/voltron-robotics), [Evaluation](https://github.com/siddk/voltron-evaluation), [Tweet](https://twitter.com/dair_ai/status/1632383327154888704?s=20) |
| 9) **Dropout Reduces Underfitting** - demonstrates that dropout can mitigate underfitting when used at the start of training; it counteracts SGD stochasticity and limits the influence of individual batches when training models.                                                                                      | [Paper](https://arxiv.org/abs/2303.01500), [Tweet](https://twitter.com/dair_ai/status/1632383328920666121?s=20)                                                                                                                 |
| 10) **Enabling Conversational Interaction with Mobile UI using Large Language Models** - an approach that enables versatile conversational interactions with mobile UIs using a single LLM.                                                                                                                              | [Paper](https://arxiv.org/abs/2209.08655), [Tweet](https://twitter.com/dair_ai/status/1632383331286253568?s=20)                                                                                                                 |

---

## Top ML Papers of the Week (Feb 20-26)

| **Paper**                                                                                                                                                                                                                                                                                                | **Links**                                                                                                                                                                                                                   |
| -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1) **LLaMA: Open and Efficient Foundation Language Models** - a 65B parameter foundation model released by Meta AI; relies on publicly available data and outperforms GPT-3 on most benchmarks despite being 10x smaller.                                                                                | [Paper](https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/), [Tweet](https://twitter.com/dair_ai/status/1629845535946420226?s=20)                                              |
| 2) **Composer: Creative and Controllable Image Synthesis with Composable Conditions** - a 5B parameter creative and controllable diffusion model trained on billions (text, image) pairs.                                                                                                                | [Paper](https://arxiv.org/abs/2302.09778), [Project](https://damo-vilab.github.io/composer-page/) , [GitHub](https://github.com/damo-vilab/composer) , [Tweet](https://twitter.com/dair_ai/status/1629845537913548802?s=20) |
| 3) **The Wisdom of Hindsight Makes Language Models Better Instruction Followers** - an alternative algorithm to train LLMs from feedback; the feedback is converted to instruction by relabeling the original one and training the model, in a supervised way, for better alignment.                     | [Paper](https://arxiv.org/abs/2302.05206), [GitHub](https://github.com/tianjunz/HIR) [Tweet](https://twitter.com/dair_ai/status/1629845539964481537?s=20)                                                                   |
| 4) **Active Prompting with Chain-of-Thought for Large Language Models** - a prompting technique to adapt LLMs to different task-specific example prompts (annotated with human-designed chain-of-thought reasoning); this process involves finding where the LLM is most uncertain and annotating those. | [Paper](https://arxiv.org/abs/2302.12246), [Code](https://github.com/shizhediao/active-prompt) [Tweet](https://twitter.com/dair_ai/status/1629845541847724033?s=20)                                                         |
| 5) **Modular Deep Learning** - a survey offering a unified view of the building blocks of modular neural networks; it also includes a discussion about modularity in the context of scaling LMs, causal inference, and other key topics in ML.                                                           | [Paper](https://arxiv.org/abs/2302.11529) , [Project](https://www.ruder.io/modular-deep-learning/), [Tweet](https://twitter.com/dair_ai/status/1629845544037228551?s=20)                                                    |
| 6) **Recitation-Augmented Language Models** - an approach that recites passages from the LLM’s own memory to produce final answers; shows high performance on knowledge-intensive tasks.                                                                                                                 | [Paper](https://arxiv.org/abs/2210.01296) , [Tweet](https://twitter.com/dair_ai/status/1629845546276995075?s=20)                                                                                                            |
| 7) **Learning Performance-Improving Code Edits** - an approach that uses LLMs to suggest functionally correct, performance-improving code edits.                                                                                                                                                         | [Paper](https://arxiv.org/abs/2302.07867), [Tweet](https://twitter.com/dair_ai/status/1629845548210561029?s=20)                                                                                                             |
| 8) **More than you've asked for: A Comprehensive Analysis of Novel Prompt Injection Threats to Application-Integrated Large Language Models** - a comprehensive analysis of novel prompt injection threats to application-integrated LLMs.                                                               | [Paper](https://arxiv.org/abs/2302.12173), [Tweet](https://twitter.com/dair_ai/status/1629845550152523777?s=20)                                                                                                             |
| 9) **Aligning Text-to-Image Models using Human Feedback** - proposes a fine-tuning method to align generative models using human feedback.                                                                                                                                                               | [Paper](https://arxiv.org/abs/2302.12192), [Tweet](https://twitter.com/dair_ai/status/1629845552039968780?s=20)                                                                                                             |
| 10) **MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes** - a memory-efficient radiance field representation for real-time view synthesis of large-scale scenes in a browser.                                                                                      | [Paper](https://arxiv.org/abs/2302.12249), [Tweet](https://twitter.com/dair_ai/status/1629845554061606915?s=20)                                                                                                             |

---

## Top ML Papers of the Week (Feb 13 - 19)

| **Paper**                                                                                                                                                                                                                                                            | **Links**                                                                                                                                                 |
| -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1) **Symbolic Discovery of Optimization Algorithms** - a simple and effective optimization algorithm that’s more memory-efficient than Adam.                                                                                                                         | [Paper](https://arxiv.org/abs/2302.06675), [Tweet](https://twitter.com/dair_ai/status/1627671313874575362?s=20)                                           |
| 2) **Transformer models: an introduction and catalog**                                                                                                                                                                                                               | [Paper](https://arxiv.org/abs/2302.07730), [Tweet](https://twitter.com/dair_ai/status/1627671315678126082?s=20)                                           |
| 3) **3D-aware Conditional Image Synthesis** - a 3D-aware conditional generative model extended with neural radiance fields for controllable photorealistic image synthesis.                                                                                          | [Project](https://www.cs.cmu.edu/~pix2pix3D/) [Tweet](https://twitter.com/dair_ai/status/1627671317355831296?s=20)                                        |
| 4) **The Capacity for Moral Self-Correction in Large Language Models** - finds strong evidence that language models trained with RLHF have the capacity for moral self-correction. The capability emerges at 22B model parameters and typically improves with scale. | [Paper](https://arxiv.org/abs/2302.07459), [Tweet](https://twitter.com/dair_ai/status/1627671319100768260?s=20)                                           |
| 5) **Vision meets RL** - uses reinforcement learning to align computer vision models with task rewards; observes large performance boost across multiple CV tasks such as object detection and colorization.                                                         | [Paper](https://arxiv.org/abs/2302.08242)                                                                                                                 |
| 6) **Language Quantized AutoEncoders: Towards Unsupervised Text-Image Alignment** - an unsupervised method for text-image alignment that leverages pretrained language models; it enables few-shot image classification with LLMs.                                   | [Paper](https://arxiv.org/abs/2302.00902) , [Code](https://github.com/lhao499/lqae) [Tweet](https://twitter.com/haoliuhl/status/1625273748629901312?s=20) |
| 7) **Augmented Language Models: a Survey** - a survey of language models that are augmented with reasoning skills and the capability to use tools.                                                                                                                   | [Paper](https://arxiv.org/abs/2302.07842), [Tweet](https://twitter.com/dair_ai/status/1627671324477820929?s=20)                                           |
| 8) **Geometric Clifford Algebra Networks** - an approach to incorporate geometry-guided transformations into neural networks using geometric algebra.                                                                                                                | [Paper](https://arxiv.org/abs/2302.06594), [Tweet](https://twitter.com/dair_ai/status/1627671326176473088?s=20)                                           |
| 9) **Auditing large language models: a three-layered approach** - proposes a policy framework for auditing LLMs.                                                                                                                                                     | [Paper](https://arxiv.org/abs/2302.08500), [Tweet](https://twitter.com/dair_ai/status/1627671327950643200?s=20)                                           |
| 10) **Energy Transformer** - a transformer architecture that replaces the sequence of feedforward transformer blocks with a single large Associate Memory model; this follows the popularity that Hopfield Networks have gained in the field of ML.                  | [Paper](https://arxiv.org/abs/2302.07253), [Tweet](https://twitter.com/dair_ai/status/1627671329561346050?s=20)                                           |

---

## Top ML Papers of the Week (Feb 6 - 12)

| **Paper**                                                                                                                                                                                                               | **Links**                                                                                                                                                                                           |
| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1) **Toolformer: Language Models Can Teach Themselves to Use Tools** - introduces language models that teach themselves to use external tools via simple API calls.                                                     | [Paper](https://arxiv.org/abs/2302.04761), [Tweet](https://twitter.com/dair_ai/status/1624832248691191808?s=20&t=ygX07dsAPDF8_jwrxZIo1Q)                                                            |
| 2) **Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents** - proposes using language models for open-world game playing.                           | [Paper](https://arxiv.org/abs/2302.01560), [Tweet](https://twitter.com/dair_ai/status/1624832250717036548?s=20&t=ygX07dsAPDF8_jwrxZIo1Q)                                                            |
| 3) **A Categorical Archive of ChatGPT Failures** - a comprehensive analysis of ChatGPT failures for categories like reasoning, factual errors, maths, and coding.                                                       | [Paper](https://arxiv.org/abs/2302.03494), [Tweet](https://twitter.com/dair_ai/status/1624832252587700230?s=20&t=ygX07dsAPDF8_jwrxZIo1Q)                                                            |
| 4) **Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery** - optimizing hard text prompts through efficient gradient-based optimization.                                       | [Paper](https://arxiv.org/abs/2302.03668), [Tweet](https://twitter.com/dair_ai/status/1624832254588465156?s=20&t=ygX07dsAPDF8_jwrxZIo1Q)                                                            |
| 5) **Data Selection for Language Models via Importance Resampling** - proposes a cheap and scalable data selection framework based on an importance resampling algorithm to improve the downstream performance of LMs.  | [Paper](https://arxiv.org/abs/2302.03169), [Tweet](https://twitter.com/dair_ai/status/1624832256400302080?s=20&t=ygX07dsAPDF8_jwrxZIo1Q)                                                            |
| 6) **Structure and Content-Guided Video Synthesis with Diffusion Models** - proposes an approach for structure and content-guided video synthesis with diffusion models.                                                | [Paper](https://arxiv.org/abs/2302.03011) , [Project](https://research.runwayml.com/gen1), [Tweet](https://twitter.com/dair_ai/status/1624832258296229889?s=20&t=ygX07dsAPDF8_jwrxZIo1Q)            |
| 7) **A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity** - performs a more rigorous evaluation of ChatGPt on reasoning, hallucination, and interactivity.      | [Paper](https://arxiv.org/abs/2302.04023), [Tweet](https://twitter.com/dair_ai/status/1624832260213026819?s=20&t=ygX07dsAPDF8_jwrxZIo1Q)                                                            |
| 8) **Noise2Music: Text-conditioned Music Generation with Diffusion Models** - proposes diffusion models to generate high-quality 30-second music clips via text prompts.                                                | [Paper](https://arxiv.org/abs/2302.03917), [Project](https://google-research.github.io/noise2music/), [Tweet](https://twitter.com/dair_ai/status/1624832262163337220?s=20&t=ygX07dsAPDF8_jwrxZIo1Q) |
| 9) **Offsite-Tuning: Transfer Learning without Full Model** - introduces an efficient, privacy-preserving transfer learning framework to adapt foundational models to downstream data without access to the full model. | [Paper](https://arxiv.org/abs/2302.04870), [Project](https://github.com/mit-han-lab/offsite-tuning), [Tweet](https://twitter.com/dair_ai/status/1624832264029831169?s=20&t=ygX07dsAPDF8_jwrxZIo1Q)  |
| 10) **Zero-shot Image-to-Image Translation** - proposes a model for zero-shot image-to-image translation.                                                                                                               | [Paper](https://arxiv.org/abs/2302.03027), [Project](https://pix2pixzero.github.io/), [Tweet](https://twitter.com/dair_ai/status/1624832265967607813?s=20&t=ygX07dsAPDF8_jwrxZIo1Q)                 |

---

## Top ML Papers of the Week (Jan 30-Feb 5)

| **Paper**                                                                                                                                                                                                                                      | **Links**                                                                                                                                                                                     |
| ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1) **REPLUG: Retrieval-Augmented Black-Box Language Models** - a retrieval-augmented LM framework that adapts a retriever to a large-scale, black-box LM like GPT-3.                                                                           | [Paper](https://arxiv.org/abs/2301.12652), [Tweet](https://twitter.com/dair_ai/status/1622261780725616641?s=20&t=ygX07dsAPDF8_jwrxZIo1Q)                                                      |
| 2) **Extracting Training Data from Diffusion Models** - shows that diffusion-based generative models can memorize images from the training data and emit them at generation time.                                                              | [Paper](https://arxiv.org/abs/2301.13188), [Tweet](https://twitter.com/dair_ai/status/1622261782738788353?s=20&t=ygX07dsAPDF8_jwrxZIo1Q)                                                      |
| 3) **The Flan Collection: Designing Data and Methods for Effective Instruction Tuning** - release a more extensive publicly available collection of tasks, templates, and methods to advancing instruction-tuned models.                       | [Paper](https://arxiv.org/abs/2301.13688), [Tweet](https://twitter.com/dair_ai/status/1622261784668241922?s=20&t=ygX07dsAPDF8_jwrxZIo1Q)                                                      |
| 4) **Multimodal Chain-of-Thought Reasoning in Language Models** - incorporates vision features to elicit chain-of-thought reasoning in multimodality, enabling the model to generate effective rationales that contribute to answer inference. | [Paper](https://arxiv.org/abs/2302.00923), [Code](https://github.com/amazon-science/mm-cot) [Tweet](https://twitter.com/dair_ai/status/1622261786559791105?s=20&t=ygX07dsAPDF8_jwrxZIo1Q)     |
| 5) **Dreamix: Video Diffusion Models are General Video Editors** - a diffusion model that performs text-based motion and appearance editing of general videos.                                                                                 | [Paper](https://arxiv.org/abs/2302.01329), [Project](https://dreamix-video-editing.github.io/), [Tweet](https://twitter.com/dair_ai/status/1622261788497657856?s=20&t=ygX07dsAPDF8_jwrxZIo1Q) |
| 6) **Benchmarking Large Language Models for News Summarization**                                                                                                                                                                               | [Paper](https://arxiv.org/abs/2301.13848) , [Tweet](https://twitter.com/dair_ai/status/1622261790326259714?s=20&t=ygX07dsAPDF8_jwrxZIo1Q)                                                     |
| 7) **Mathematical Capabilities of ChatGPT** - investigates the mathematical capabilities of ChatGPT on a new holistic benchmark called GHOSTS.                                                                                                 | [Paper](https://arxiv.org/abs/2301.13867), [Tweet](https://twitter.com/dair_ai/status/1622261792238886913?s=20&t=ygX07dsAPDF8_jwrxZIo1Q)                                                      |
| 8) **Emergence of Maps in the Memories of Blind Navigation Agents** - trains an AI agent to navigate purely by feeling its way around; no use of vision, audio, or any other sensing (as in animals).                                          | [Paper](https://arxiv.org/abs/2301.13261), [Project](https://wijmans.xyz/publication/eom/), [Tweet](https://twitter.com/dair_ai/status/1622261793987989507?s=20&t=ygX07dsAPDF8_jwrxZIo1Q)     |
| 9) **SceneDreamer: Unbounded 3D Scene Generation from 2D Image Collections** - a generative model that synthesizes large-scale 3D landscapes from random noises.                                                                               | [Paper](https://arxiv.org/abs/2302.01330), [Tweet](https://twitter.com/dair_ai/status/1622261795925671936?s=20&t=ygX07dsAPDF8_jwrxZIo1Q)                                                      |
| 10) **Large Language Models Can Be Easily Distracted by Irrelevant Context** - finds that many prompting techniques fail when presented with irrelevant context for arithmetic reasoning.                                                      | [Paper](https://arxiv.org/abs/2302.00093), [Tweet](https://twitter.com/dair_ai/status/1622261798379429888?s=20&t=ygX07dsAPDF8_jwrxZIo1Q)                                                      |

---

## Top ML Papers of the Week (Jan 23-29)

| **Paper**                                                                                                                                                                                                                                                            | **Links**                                                                                                                                                                                                                                                                                                            |
| -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1) **MusicLM: Generating Music From Text** - a generative model for generating high-fidelity music from text descriptions.                                                                                                                                           | [Paper](https://arxiv.org/abs/2301.11325), [Tweet](https://twitter.com/dair_ai/status/1619716425761042436?s=20&t=ygX07dsAPDF8_jwrxZIo1Q)                                                                                                                                                                             |
| 2) **Hungry Hungry Hippos: Towards Language Modeling with State Space Models** - an approach to reduce the gap, in terms of performance and hardware utilization, between state space models and attention for language modeling.                                    | [Paper](https://arxiv.org/abs/2212.14052), [Tweet](https://twitter.com/dair_ai/status/1619716427879174144?s=20&t=ygX07dsAPDF8_jwrxZIo1Q)                                                                                                                                                                             |
| 3) **A Watermark for Large Language Models** - a watermarking framework for proprietary language models.                                                                                                                                                             | [Paper](https://arxiv.org/abs/2301.10226), [Tweet](https://twitter.com/dair_ai/status/1619716430127308800?s=20&t=ygX07dsAPDF8_jwrxZIo1Q)                                                                                                                                                                             |
| 4) **Text-To-4D Dynamic Scene Generation** - a new text-to-4D model for dynamic scene generation from input text.                                                                                                                                                    | [Paper](https://arxiv.org/abs/2301.11280), [GitHub](https://make-a-video3d.github.io/), [Tweet](https://twitter.com/dair_ai/status/1619718845018828801?s=20&t=ygX07dsAPDF8_jwrxZIo1Q)                                                                                                                                |
| 5) **ClimaX: A foundation model for weather and climate** - a foundation model for weather and climate, including many capabilities for atmospheric science tasks.                                                                                                   | [Paper](https://arxiv.org/abs/2301.10343), [Tweet](https://twitter.com/tungnd_13/status/1618642574427959296?s=20&t=ygX07dsAPDF8_jwrxZIo1Q), [Blog](https://www.microsoft.com/en-us/research/group/autonomous-systems-group-robotics/articles/introducing-climax-the-first-foundation-model-for-weather-and-climate/) |
| 6) **Open Problems in Applied Deep Learning** - If you're looking for interesting open problems in DL, this is a good reference. Not sure if intentional but it also looks useful to get a general picture of current trends in deep learning with \~300 references. | [Paper](https://arxiv.org/abs/2301.11316) , [Tweet](https://twitter.com/dair_ai/status/1619719063915339777?s=20&t=ygX07dsAPDF8_jwrxZIo1Q)                                                                                                                                                                            |
| 7) **DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature** - an approach for zero-shot machine-generated text detection. Uses raw log probabilities from the LLM to determine if the passage was sampled from it.                      | [Paper](https://arxiv.org/abs/2301.11305), [Tweet](https://twitter.com/dair_ai/status/1619719169758613504?s=20&t=ygX07dsAPDF8_jwrxZIo1Q)                                                                                                                                                                             |
| 8) **StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis** - a new model that aims to regain the competitiveness of GANs for fast large-scale text-to-image synthesis.                                                              | [Paper](https://arxiv.org/abs/2301.09515), [Project](https://sites.google.com/view/stylegan-t/), [Code](https://github.com/autonomousvision/stylegan-t) [Tweet](https://twitter.com/dair_ai/status/1619719293779976193?s=20&t=ygX07dsAPDF8_jwrxZIo1Q)                                                                |
| 9) **Large language models generate functional protein sequences across diverse families** - an LLM that can generate protein sequences with a predictable function across large protein families.                                                                   | [Paper](https://www.nature.com/articles/s41587-022-01618-2), [Tweet](https://twitter.com/dair_ai/status/1619719404618645511?s=20&t=ygX07dsAPDF8_jwrxZIo1Q)                                                                                                                                                           |
| 10) **The Impossibility of Parallelizing Boosting** - investigates the possibility of parallelizing boosting.                                                                                                                                                        | [Paper](https://arxiv.org/abs/2301.09627), [Tweet](https://twitter.com/dair_ai/status/1619719511867015168?s=20&t=ygX07dsAPDF8_jwrxZIo1Q)                                                                                                                                                                             |

---

## Top ML Papers of the Week (Jan 16-22)

| **Paper**                                                                                                                                                                                                                        | **Links**                                                                                                                                                                           |
| -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1) **Google AI Research Recap (2022 Edition)** - an excellent summary of some notable research Google AI did in 2022.                                                                                                            | [Blog](https://ai.googleblog.com/2023/01/google-research-2022-beyond-language.html), [Tweet](https://twitter.com/JeffDean/status/1615796030611820545?s=20&t=vUEC8AZmrOJnVxuYIEJs5A) |
| 2) **Dissociating language and thought in large language models: a cognitive perspective** - a review paper on the capabilities of LLMs from a cognitive science perspective.                                                    | [Paper](https://arxiv.org/abs/2301.06627), [Tweet](https://twitter.com/neuranna/status/1615737072207400962?s=20&t=5iWUK4z_rp1NWst7JRbnwg)                                           |
| 3) **Human-Timescale Adaptation in an Open-Ended Task Space** - an agent trained at scale that leads to a general in-content learning algorithm able to adapt to open-ended embodied 3D problems.                                | [Paper](https://arxiv.org/abs/2301.07608), [Tweet](https://twitter.com/FeryalMP/status/1616035293064462338?s=20&t=RN0YZFAXWr-uH2dT2ZTSqQ)                                           |
| 4) **AtMan: Understanding Transformer Predictions Through Memory Efficient Attention Manipulation** - an approach to help provide explanations of generative transformer models through memory-efficient attention manipulation. | [Paper](https://arxiv.org/abs/2301.08110), [Tweet](https://twitter.com/JonasAndrulis/status/1616722810608427008?s=20&t=vUEC8AZmrOJnVxuYIEJs5A)                                      |
| 5) **Everything is Connected: Graph Neural Networks** - short overview of key concepts in graph representation learning.                                                                                                         | [Paper](https://arxiv.org/abs/2301.08210), [Tweet](https://twitter.com/PetarV_93/status/1616379369953394688?s=20&t=AqTVY30Y7IZCultzwnqBPA)                                          |
| 6) **GLIGEN: Open-Set Grounded Text-to-Image Generation** - an approach that extends the functionality of existing pre-trained text-to-image diffusion models by enabling conditioning on grounding inputs.                      | [Paper](https://arxiv.org/abs/2301.07093), [Tweet](https://twitter.com/hardmaru/status/1615766551113744384?s=20&t=wx0Y18oSmW0YenXjKRAdnA), [Project](https://gligen.github.io/)     |
| 7) **InstructPix2Pix: Learning to Follow Image Editing Instructions** - proposes a method with the capability of editing images from human instructions.                                                                         | [Paper](https://arxiv.org/abs/2211.09800), [Tweet](https://twitter.com/_akhaliq/status/1615947919286276096?s=20&t=pbRTn8DaPeQFApQ9okkdRg)                                           |
| 8) **Dataset Distillation: A Comprehensive Review**                                                                                                                                                                              | [Paper](https://arxiv.org/abs/2301.07014), [Tweet](https://twitter.com/omarsar0/status/1615745724473540609?s=20&t=r-pwuB6EhbZLXa5R6mL3NQ)                                           |
| 9) **Learning-Rate-Free Learning by D-Adaptation** - a new method for automatically adjusting the learning rate during training, applicable to more than a dozen diverse ML problems.                                            | [Paper](https://arxiv.org/abs/2301.07733), [Tweet](https://twitter.com/aaron_defazio/status/1616453609956478977?s=20&t=hGWDXu4sT5f1KcH-X1IL9g)                                      |
| 10) **RecolorNeRF: Layer Decomposed Radiance Field for Efficient Color Editing of 3D Scenes** - a user-friendly color editing approach for the neural radiance field to achieve a more efficient view-consistent recoloring.     | [Paper](https://arxiv.org/abs/2301.07958), [Tweet](https://twitter.com/_akhaliq/status/1616265465843548160?s=20&t=duiLmtDvxCwkFmw23rYDmQ)                                           |

---

## Top ML Papers of the Week (Jan 9-15)

| **Paper**                                                                                                                                                                                                                                                                                                           | **Links**                                                                                                                                                                                  |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| 1) **Mastering Diverse Domains through World Models** - a general algorithm to collect diamonds in Minecraft from scratch without human data or curricula, a long-standing challenge in AI.                                                                                                                         | [Paper](https://arxiv.org/abs/2301.04104v1), [Tweet](https://twitter.com/dair_ai/status/1614676677757661185?s=20&t=3GITA7PeX7pGwrqvt97bYQ)                                                 |
| 2) **Tracr: Compiled Transformers as a Laboratory for Interpretability** - a compiler for converting RASP programs into transformer weights. This way of constructing NNs weights enables the development and evaluation of new interpretability tools.                                                             | [Paper](https://arxiv.org/abs/2301.05062), [Tweet](https://twitter.com/dair_ai/status/1614676680165187584?s=20&t=3GITA7PeX7pGwrqvt97bYQ), [Code](https://github.com/deepmind/tracr)        |
| 3) **Multimodal Deep Learning** - multimodal deep learning is a new book published on ArXiv.                                                                                                                                                                                                                        | [Book](https://arxiv.org/abs/2301.04856), [Tweet](https://twitter.com/dair_ai/status/1614676682555670528?s=20&t=3GITA7PeX7pGwrqvt97bYQ)                                                    |
| 4) **Forecasting Potential Misuses of Language Models for Disinformation Campaigns—and How to Reduce Risk** - new work analyzing how generative LMs could potentially be misused for disinformation and how to mitigate these types of risks.                                                                       | [Paper](https://openai.com/blog/forecasting-misuse/), [Tweet](https://twitter.com/dair_ai/status/1614676684984156160?s=20&t=3GITA7PeX7pGwrqvt97bYQ)                                        |
| 5) **Why do Nearest Neighbor Language Models Work?** - empirically identifies reasons why retrieval-augmented LMs (specifically k-nearest neighbor LMs) perform better than standard parametric LMs.                                                                                                                | [Paper](https://arxiv.org/abs/2301.02828), [Code](https://github.com/frankxu2004/knnlm-why), [Tweet](https://twitter.com/dair_ai/status/1614676687597469696?s=20&t=3GITA7PeX7pGwrqvt97bYQ) |
| 6) **Memory Augmented Large Language Models are Computationally Universal** - investigates the use of existing LMs (e.g, Flan-U-PaLM 540B) combined with associative read-write memory to simulate the execution of a universal Turing machine.                                                                     | [Paper](https://arxiv.org/abs/2301.04589) , [Tweet](https://twitter.com/dair_ai/status/1614676689908277252?s=20&t=3GITA7PeX7pGwrqvt97bYQ)                                                  |
| 7) **A Survey on Transformers in Reinforcement Learning** - transformers for RL will be a fascinating research area to track. The same is true for the reverse direction (RL for Transformers)... a notable example: using RLHF to improve LLMs (e.g., ChatGPT).                                                    | [Paper](https://arxiv.org/abs/2301.03044), [Tweet](https://twitter.com/dair_ai/status/1614676692538105860?s=20&t=3GITA7PeX7pGwrqvt97bYQ)                                                   |
| 8) **Scaling Laws for Generative Mixed-Modal Language Models** - introduces scaling laws for generative mixed-modal language models.                                                                                                                                                                                | [Paper](https://arxiv.org/abs/2301.03728), [Tweet](https://twitter.com/dair_ai/status/1614676694920531969?s=20&t=3GITA7PeX7pGwrqvt97bYQ)                                                   |
| 9) **DeepMatcher: A Deep Transformer-based Network for Robust and Accurate Local Feature Matching** - a transformer-based network showing robust local feature matching, outperforming the state-of-the-art methods on several benchmarks.                                                                          | [Paper](https://arxiv.org/abs/2301.02993), [Tweet](https://twitter.com/dair_ai/status/1614676697516752898?s=20&t=3GITA7PeX7pGwrqvt97bYQ)                                                   |
| 10) **Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement** - addresses the time series forecasting problem with generative modeling; involves a bidirectional VAE backbone equipped with diffusion, denoising for prediction accuracy, and disentanglement for model interpretability. | [Paper](https://arxiv.org/abs/2301.03028), [Tweet](https://twitter.com/dair_ai/status/1614676699915980804?s=20&t=3GITA7PeX7pGwrqvt97bYQ)                                                   |

---

## Top ML Papers of the Week (Jan 1-8)

| **Paper**                                                                                                                                                                                                                                                                                                         | **Links**                                                                                                                                                                                                                                      |
| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1) **Muse: Text-To-Image Generation via Masked Generative Transformers** - introduces Muse, a new text-to-image generation model based on masked generative transformers; significantly more efficient than other diffusion models like Imagen and DALLE-2.                                                       | [Paper](https://arxiv.org/abs/2301.00704), [Project](https://muse-model.github.io/), [Code](https://github.com/lucidrains/muse-maskgit-pytorch), [Tweet](https://twitter.com/dair_ai/status/1612153095772938241?s=20&t=ChwZWzSmoRlZKnD54fsV6w) |
| 2) **VALL-E Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers** - introduces VALL-E, a text-to-audio model that performs state-of-the-art zero-shot performance; the text-to-speech synthesis task is treated as a conditional language modeling task.                                       | [Project](https://valle-demo.github.io/), [Tweet](https://twitter.com/dair_ai/status/1612153097962328067?s=20&t=ChwZWzSmoRlZKnD54fsV6w)                                                                                                        |
| 3) **Rethinking with Retrieval: Faithful Large Language Model Inference** - shows the potential of enhancing LLMs by retrieving relevant external knowledge based on decomposed reasoning steps obtained through chain-of-thought prompting.                                                                      | [Paper](https://arxiv.org/abs/2301.00303), [Tweet](https://twitter.com/dair_ai/status/1612153100114055171?s=20&t=ChwZWzSmoRlZKnD54fsV6w)                                                                                                       |
| 4) **SparseGPT: Massive Language Models Can Be Accurately Pruned In One-Shot** - presents a technique for compressing large language models while not sacrificing performance; "pruned to at least 50% sparsity in one-shot, without any retraining."                                                             | [Paper](https://arxiv.org/abs/2301.00774), [Tweet](https://twitter.com/dair_ai/status/1612153102513360901?s=20&t=ChwZWzSmoRlZKnD54fsV6w)                                                                                                       |
| 5) **ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders** - a performant model based on a fully convolutional masked autoencoder framework and other architectural improvements. CNNs are sticking back!                                                                                     | [Paper](https://arxiv.org/abs/2301.00808), [Code](https://github.com/facebookresearch/convnext-v2), [Tweet](https://twitter.com/dair_ai/status/1612153104329281538?s=20&t=ChwZWzSmoRlZKnD54fsV6w)                                              |
| 6) **Large Language Models as Corporate Lobbyists** - with more capabilities, we are starting to see a wider range of applications with LLMs. This paper utilized large language models for conducting corporate lobbying activities.                                                                             | [Paper](https://arxiv.org/abs/2301.01181) , [Code](https://github.com/JohnNay/llm-lobbyist), [Tweet](https://twitter.com/dair_ai/status/1612153106355130372?s=20&t=ChwZWzSmoRlZKnD54fsV6w)                                                     |
| 7) **Superposition, Memorization, and Double Descent** - aims to better understand how deep learning models overfit or memorize examples; interesting phenomena observed; important work toward a mechanistic theory of memorization.                                                                             | [Paper](https://transformer-circuits.pub/2023/toy-double-descent/index.html), [Tweet](https://twitter.com/dair_ai/status/1612153108460892160?s=20&t=ChwZWzSmoRlZKnD54fsV6w)                                                                    |
| 8) **StitchNet: Composing Neural Networks from Pre-Trained Fragments** - new idea to create new coherent neural networks by reusing pretrained fragments of existing NNs. Not straightforward but there is potential in terms of efficiently reusing learned knowledge in pre-trained networks for complex tasks. | [Paper](https://arxiv.org/abs/2301.01947), [Tweet](https://twitter.com/dair_ai/status/1612153110452903936?s=20&t=ChwZWzSmoRlZKnD54fsV6w)                                                                                                       |
| 9) **Iterated Decomposition: Improving Science Q\&A by Supervising Reasoning Processes** - proposes integrated decomposition, an approach to improve Science Q\&A through a human-in-the-loop workflow for refining compositional LM programs.                                                                    | [Paper](https://arxiv.org/abs/2301.01751), [Code](https://github.com/oughtinc/ice) [Tweet](https://twitter.com/dair_ai/status/1612153112638402562?s=20&t=ChwZWzSmoRlZKnD54fsV6w)                                                               |
| 10) **A Succinct Summary of Reinforcement Learning** - a nice overview of some important ideas in RL.                                                                                                                                                                                                             | [Paper](https://arxiv.org/abs/2301.01379), [Tweet](https://twitter.com/dair_ai/status/1612153114773053446?s=20&t=ChwZWzSmoRlZKnD54fsV6w)                                                                                                       |

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