AI Papers of the Week — 2023

This page collects every weekly issue of AI Papers of the Week from 2023. For other years, see the main index.

Top AI Papers of the Week (December 25 - December 31)

Paper	Links
1) CogAgent - Tsinghua's CogAgent is an 18B-parameter visual-language model purpose-built for GUI understanding and navigation, with unusually high input resolution. ● High-res GUI input: Supports 1120x1120 input resolution via a dedicated high-res cross-module, letting it read small fonts and dense UI elements that typical VLMs blur out. ● Dual-tower vision: Combines a low-res general vision encoder with a high-res cross-module, balancing context understanding with fine-grained icon/text perception. ● Broad capabilities: Handles visual Q&A, visual grounding, and end-to-end GUI agent tasks on web and desktop, positioning as a general GUI backbone. ● SoTA VQA: Achieves state-of-the-art on 5 text-rich (e.g., OCR-heavy) and 4 general VQA benchmarks, covering document, chart, and scene understanding.	Paper, Tweet
2) From Gemini to Q-Star - A 300+-paper survey mapping the state of Generative AI and the research frontiers that followed the Gemini + rumored Q* news cycle. ● Broad coverage: Surveys developments across language, vision, audio, and multimodal generative systems, treating Gen AI as a unified field rather than siloed modalities. ● Computational challenges: Catalogs scalability, efficiency, and alignment challenges currently gating further progress, including training compute, inference serving, and evaluation. ● Real-world applications: Reviews Gen AI impact across healthcare, finance, and education, highlighting where genuine deployment signals diverge from hype. ● Future directions: Identifies agent frameworks, reasoning, grounded multimodality, and alignment as the most live research areas heading into 2024.	Paper, Tweet
3) PromptBench - A unified library for comprehensive evaluation and analysis of LLMs that consolidates multiple evaluation concerns under one roof. ● Prompt-construction tooling: Ships with utilities for prompt construction, prompt engineering, and dataset/model loading, covering the end-to-end LLM evaluation workflow. ● Adversarial prompt attacks: Built-in adversarial prompt-attack capabilities let users stress-test LLMs against perturbations rather than just measuring clean accuracy. ● Dynamic evaluation: Supports dynamic evaluation protocols to detect dataset contamination and measure robustness beyond static benchmark numbers. ● Unified interface: Replaces the ad-hoc evaluation scripts many teams maintain with a consistent API, reducing friction when comparing across models and prompt variants.	Paper, Tweet
4) Exploiting Novel GPT-4 APIs - A red-team study of three newer GPT-4 API surfaces - fine-tuning, function calling, and knowledge retrieval - that reveals each introduces new attack vectors. ● Fine-tuning strips safeguards: As few as 15 harmful examples - or even 100 benign examples - fine-tuned into GPT-4 is enough to remove core safety behaviors. ● Function-call schema leakage: GPT-4 Assistants can be coerced into divulging their function-call schemas and then tricked into executing arbitrary function calls. ● Retrieval hijacking: The knowledge-retrieval endpoint is vulnerable to prompt injection via documents in the retrieval corpus, letting attackers steer model behavior through uploaded content. ● Policy implication: Expanding API surface area introduces alignment risks that weren't present for text-only completions, and API providers need surface-specific defenses rather than relying on base-model alignment.	Paper, Tweet
5) Fact Recalling in LLMs - A mechanistic-interpretability study showing that early MLP layers function as a lookup table for factual recall. ● Athletes-to-sports task: Scoped to how Pythia 2.8B recalls which of 3 different sports various athletes play - a clean task for dissecting a single type of factual recall. ● Early MLPs as lookup table: Early MLP layers perform a structured lookup rather than distributed reasoning, with specific neurons keyed to entity-attribute pairs. ● Multi-token embedding view: Recommends treating factual knowledge recall as operating over multi-token embeddings rather than single-token representations. ● Interpretability payoff: Provides a concrete, testable account of where and how facts live inside transformers, enabling targeted editing and auditing of parametric memory.	Paper, Tweet
6) Generative AI for Math (OpenWebMath / MathPile) - Releases a diverse, high-quality math-centric corpus of ~9.5B tokens designed for training math-capable foundation models. ● 9.5B-token corpus: Curated from mathematical content across the web, textbooks, papers, and Q&A, rebalanced for math-specific token distribution. ● Quality filtering: Applies math-specific filtering to surface content dense in symbolic notation, proofs, and problem solutions rather than surface-level mentions of math. ● Diverse sources: Explicitly mixes proof-heavy formal math with applied problem-solving to avoid over-fitting to any single mathematical register. ● Training signal: Positioned as a drop-in pretraining or continual-pretraining corpus to lift math reasoning in existing LLMs without changing the architecture.	Paper, Tweet
7) Principled Instructions Are All You Need - Distills effective LLM prompting into 26 guiding principles and validates them across multiple model families. ● 26 principles: Covers prompt structure, audience specification, example selection, formatting, role assignment, and stepwise decomposition. ● Broad model validation: Tested on LLaMA-1/2 (7B, 13B, 70B) and GPT-3.5/4, finding the principles generalize across scales and families. ● Both small and large benefits: Smaller models benefit more from structured prompting (higher variance reduction), while larger models benefit in absolute accuracy on harder tasks. ● Practical reference: Functions as a cheat-sheet for practitioners, converting scattered prompting folklore into testable recipes.	Paper, Tweet
8) Survey of Reasoning with Foundation Models - A comprehensive survey of reasoning with foundation models, covering tasks, methods, benchmarks, and future directions. ● Task coverage: Surveys math reasoning, commonsense reasoning, logical reasoning, symbolic reasoning, and multimodal reasoning - showing how each evolves with model scale. ● Methodology catalog: Covers prompting techniques (CoT, ToT, self-consistency), fine-tuning strategies, and neurosymbolic approaches under a unified framework. ● Benchmarks: Systematizes the reasoning benchmarks landscape and flags contamination and robustness concerns specific to reasoning evaluation. ● Adjacencies: Discusses how multimodal learning, autonomous agents, and super-alignment research intersect with and extend the reasoning agenda.	Paper, Tweet
9) LLaRA - LLaRA adapts a decoder-only LLM for dense retrieval via two tailored pretext tasks that leverage text embeddings from the LLM itself. ● EBAE pretext task: Embedding-Based Auto-Encoding uses LLM embeddings to reconstruct tokens of the input sentence, aligning the embedding space with semantic content. ● EBAR pretext task: Embedding-Based Auto-Regression predicts tokens of the next sentence from the current embedding, injecting discourse-level signal into retrieval embeddings. ● LLaMA 2 7B base: A LLaMA 2-7B base model is adapted into a retriever with these pretext tasks, yielding significant gains on MSMARCO and BEIR. ● Decoder retrievers validated: Provides another data point that decoder-only LLMs, with the right adaptation, rival specialized encoder retrievers - a theme that continued through 2024.	Paper
10) Gemini vs GPT-4V - A qualitative side-by-side comparison of Gemini and GPT-4V across vision-language tasks, documenting systematic behavioral differences. ● Head-to-head cases: Evaluates both models on a curated set of tasks covering document understanding, chart reading, everyday scenes, and multi-image reasoning. ● GPT-4V style: Produces precise, succinct answers with strong preference for brevity and factual minimalism. ● Gemini style: Returns more expansive, narrative answers frequently accompanied by relevant images and links - leveraging its deeper integration with search. ● Complementary strengths: Concludes that the models are substitutable for many core VLM tasks but differ sharply on response length, multimedia, and augmentation patterns.	Paper, Tweet

Top AI Papers of the Week (December 18 - December 24)

Paper	Links
1) Gemini's Language Abilities - CMU's impartial, reproducible evaluation of Gemini Pro against GPT and Mixtral across standard LLM benchmarks. ● Reproducible methodology: Provides an open, reproducible evaluation pipeline - a response to concerns about Google's own Gemini launch benchmarks being hard to independently verify. ● Gemini Pro vs. GPT 3.5 Turbo: Gemini Pro achieves comparable but slightly lower accuracy than GPT 3.5 Turbo, countering marketing claims of broad parity on language tasks. ● Gemini & GPT beat Mixtral: Both Gemini and GPT outperform Mixtral on these benchmarks, suggesting open mixture-of-experts has not yet closed the gap to frontier proprietary models. ● Evaluation norms: Positioned as evidence that independent replications remain essential, and that first-party model reports shouldn't be the final word on comparative capability.	Paper, Tweet
2) PowerInfer - A high-speed LLM inference engine for consumer GPUs that exploits sparse neuron activation patterns to run large models on commodity hardware. ● Hot/cold neurons: Analysis shows that a small fraction of "hot" neurons activate on most inputs while the majority of "cold" neurons activate rarely - a power-law pattern across many LLMs. ● GPU-CPU hybrid: Hot neurons are preloaded onto the GPU for fast access, while cold neurons live on the CPU and are computed lazily, dramatically reducing GPU memory pressure. ● Reduced memory + transfer: This split reduces both GPU memory demand and the CPU-GPU data transfer that typically dominates hybrid inference cost. ● 11x speedup over llama.cpp: Achieves up to ~11x faster token generation than llama.cpp on a single consumer GPU for OPT-175B-class models - a step-change for local deployment.	Paper, Tweet
3) Antibiotic Discovery with Graph Deep Learning (Nature) - MIT researchers use explainable graph neural networks to discover a new structural class of antibiotics. ● Graph neural networks: Trains GNNs on molecular graphs to predict antibiotic activity, with explainability layers that surface chemical substructures driving predictions. ● Explainable discovery: Unlike black-box property predictors, the explanation module identifies substructures underlying antibiotic activity - a feature drug chemists can actually use. ● New structural class: The discovered compounds belong to a novel structural class, not a variant of existing antibiotic scaffolds - an unusually strong generalization signal. ● Real-world pipeline: Demonstrates end-to-end pipeline from GNN prediction to wet-lab validation, reinforcing explainable ML as a practical discovery tool for biomedicine.	Paper, Tweet
4) VideoPoet - Google Research's VideoPoet is a large language model for zero-shot video generation that treats video as just another token stream. ● Unified token stream: Uses multiple tokenizers to map video, image, audio, and text into a shared discrete token space for a single autoregressive model. ● Zero-shot task variety: The same model handles image-to-video, video stylization, video-to-audio, and text-to-video without task-specific fine-tuning. ● Language-model paradigm: Demonstrates that a plain autoregressive LM, given the right tokenizers, can handle video generation - challenging the diffusion-everywhere default for video. ● Temporal consistency: Produces videos with reasonable motion coherence over short durations, a meaningful milestone for LM-based video generation.	Paper, Tweet_
5) AppAgent - Introduces an LLM-based multimodal agent that operates real smartphone apps through touch actions and screenshots. ● Multimodal control: The agent reads the phone screen (visual input) and issues low-level touch actions (tap, swipe, type), operating apps the way humans do rather than via APIs. ● Two learning modes: Learns new apps either via autonomous exploration (discovering functionality through self-play) or by observing human demonstrations. ● Cross-app generality: Demonstrates proficiency across email, social media, shopping, and creative apps, suggesting that multimodal LLMs can generalize across smartphone UIs. ● Early mobile-agent blueprint: An early example of the on-device multimodal agent pattern that would become a major 2024 deployment theme.	Paper, Tweet_
6) LLM in a Flash - Apple researchers show how to run LLMs larger than available DRAM by streaming weights from flash storage on demand. ● Flash as swap: Stores model weights on flash and streams only the rows/columns needed per forward pass into DRAM, exploiting the sparsity of relevant parameters. ● 2x DRAM headroom: Enables running models up to 2x the size of available DRAM without catastrophic slowdown, critical for on-device deployment where memory is tight. ● Major speedups vs. naive loading: 4-5x faster on CPU and 20-25x faster on GPU compared to naive parameter loading, thanks to selective transfer and row-column bundling. ● On-device LLM groundwork: Directly enabled Apple's later on-device LLM plans by showing that flash-based streaming can make phone-scale LLM inference practical.	Paper, Tweet_
7) ReST Meets ReAct - Proposes a ReAct-style agent that improves itself via reinforced self-training on its own reasoning traces. ● Self-critique ReAct: A ReAct-style agent with a self-critique step that evaluates its own reasoning and answers, generating a filterable trace dataset. ● ReST-style iterative RL: Uses growing-batch RL from AI feedback to iteratively fine-tune on the agent's successful reasoning traces, improving over rounds without human labels. ● Human-label-free: Minimizes human involvement; synthetic data with self-improvement from AI feedback is the primary training signal throughout. ● Distillation to small models: The improved agent can be distilled into models 1-2 orders of magnitude smaller with comparable performance, dramatically cutting inference cost.	Paper, Tweet_
8) Adversarial Attacks on GPT-4 - Demonstrates that a trivially simple random-search procedure can jailbreak GPT-4 with high reliability. ● Adversarial suffix: Appends a suffix to a harmful request and iteratively perturbs it, keeping changes that increase the log-probability of the response starting with "Sure". ● No gradients needed: Operates purely via the API in a black-box setting, without model gradients or weights - a much lower bar than prior white-box jailbreak work. ● Strong success rate: Achieves high attack-success rates on GPT-4 with a small number of API calls, despite ongoing alignment efforts. ● Alignment implication: Shows that current safety training is still vulnerable to near-trivial optimization attacks, pointing to the need for stronger behavioral defenses.	Paper, Tweet_
9) RAG for LLMs - A broad survey of Retrieval-Augmented Generation research, organizing the rapidly growing literature into a coherent map. ● Three-paradigm taxonomy: Organizes RAG approaches into Naive RAG, Advanced RAG (pre/post-retrieval enhancements), and Modular RAG (orchestrated component-based systems). ● Core components: Reviews retrievers, generators, and augmentation strategies separately, clarifying which design choices sit in which component. ● Evaluation and datasets: Catalogs RAG-specific benchmarks and evaluation metrics, surfacing the still-uneven state of RAG evaluation. ● Frontier directions: Highlights agentic retrieval, multimodal RAG, and long-context RAG as the key research areas driving the 2024 RAG landscape.	Paper, Tweet_
10) BabyLLM Challenge Findings - Reports results from a challenge on sample-efficient pretraining using a developmentally plausible corpus. ● Constrained pretraining: Participants pretrain on a small, child-directed-style corpus rather than on internet-scale data, testing how efficiently models can learn from limited input. ● LTG BERT wins: The winning submission, LTG BERT, beat Llama 2 70B on 3 of 4 evaluations despite vastly less training data. ● Data preprocessing pays: Strong-performing entries relied heavily on data preprocessing and training on shorter contexts, challenging assumptions about long-context training for small data. ● Cognitive-science bridge: Provides an empirical platform connecting language-model training to developmental psycholinguistics, informing both fields.	Paper, Tweet_

Top AI Papers of the Week (December 11 - December 17)

Paper	Links
1) FunSearch - DeepMind's FunSearch uses LLMs as a mutation operator in an evolutionary loop to discover genuinely new mathematical knowledge. ● LLM + evaluator loop: Combines a pretrained LLM that proposes candidate programs with a systematic evaluator that scores them, iteratively evolving low-scoring programs into high-scoring ones. ● New math discoveries: Produces novel solutions to open problems in combinatorics, including cap-set and online bin-packing, not memorized from the training data. ● Hallucination mitigation: The evaluator acts as a hard filter - only programs that actually work are kept - so LLM hallucinations don't propagate into the "discovered" knowledge. ● General recipe: Positions LLM-in-the-loop search as a general tool for scientific discovery beyond math, applicable wherever candidates can be automatically scored.	Paper, Tweet
2) Weak-to-Strong Generalization - OpenAI's superalignment team shows that weak supervisors can still elicit capabilities from much stronger models - a first empirical signal for scalable oversight. ● Weak-to-strong setup: A weak model (e.g., GPT-2) generates labels, and a strong pretrained model (e.g., GPT-4) is fine-tuned on those labels - an analog of humans supervising superhuman AI. ● Better than the supervisor: Naively fine-tuning the strong model on weak-model labels often yields a model better than the supervisor itself, demonstrating useful capability elicitation. ● ~GPT-3.5 from GPT-2 supervision: Fine-tuning GPT-4 with GPT-2-level supervision recovers close to GPT-3.5-level performance on NLP tasks - a surprising amount of capability without strong labels. ● Superalignment signal: Offers an early empirical footing for the bet that humans can align superhuman systems using their own (weaker) judgments - provided the right training recipe.	Paper, Tweet
3) Audiobox - Meta's Audiobox is a unified flow-matching audio model that generates speech, sound effects, and music from natural-language and example prompts. ● Unified audio generation: Single model handles speech, sound, and music - ending the typical pattern of one model per audio modality. ● Description + example prompting: Supports both natural-language descriptions and reference-audio examples for style control, letting users mix semantic and acoustic conditioning. ● Self-supervised infilling: Adapts a self-supervised infilling objective to pretrain on large unlabeled audio, reducing dependence on scarce labeled speech/music datasets. ● Novel voice/styles: Unlocks generation of novel vocal and acoustic styles by interpolating in the learned audio space, going beyond reproduction of training-set styles.	Paper, Tweet
4) Mathematical LLMs Survey - A survey on the progress of LLMs on mathematical reasoning tasks, covering methods, benchmarks, and open problems. ● Task taxonomy: Covers math word problem solving, symbolic reasoning, and theorem proving, showing which capabilities emerge at which model scales. ● Methods landscape: Reviews prompting techniques (CoT, PoT, ToT, self-verification) alongside fine-tuning and tool-use approaches. ● Dataset reference: Catalogs the dominant math benchmarks (GSM8K, MATH, MiniF2F, etc.) and their evaluation methodologies. ● Frontier problems: Highlights reasoning-faithfulness, formal-vs-informal math integration, and reward-model design as the key open questions.	Paper, Tweet
5) LLM360 - LLM360 is a framework for fully transparent open-source LLM development, with everything from data to training dynamics released. ● End-to-end transparency: Ships training code, the pretraining corpus, intermediate checkpoints, evaluation code, and analyses - going well beyond the "just weights" openness of earlier "open" LLMs. ● Two 7B models: Releases AMBER (general) and CRYSTALCODER (code-specialized) 7B models pretrained from scratch under the framework. ● Enables training-dynamics research: Intermediate checkpoints let researchers study loss trajectories, emergent capabilities, and data-effect ablations - typically only possible inside frontier labs. ● Standard for openness: Pushes the community's definition of "open-source LLM" from weights to a full training-pipeline standard.	Paper, Tweet
6) LLMs in Medicine - A comprehensive survey (300+ papers) of LLMs applied to medicine, from clinical tasks to biomedical research. ● Principles and applications: Covers the core principles of medical LLMs and their applications across clinical decision support, patient communication, medical education, and biomedical research. ● Benchmark coverage: Reviews medical QA benchmarks (MedQA, PubMedQA, MedMCQA, etc.) and their limitations for real clinical settings. ● Challenges: Identifies challenges specific to medicine including hallucination in clinical advice, privacy, regulatory compliance, and equity/bias concerns. ● Deployment considerations: Discusses what's required for safe deployment, including evaluation, monitoring, and the role of clinician oversight.	Paper, Tweet
7) Beyond Human Data (ReST-EM) - DeepMind's ReST-EM shows that model-generated data plus a reward function can substantially reduce dependence on human-generated data. ● Expectation-Maximization framing: Generates candidate solutions from the current model, filters using a reward/verifier, and fine-tunes on the filtered set - repeat. ● Verifiable rewards: Uses automatic verifiers (e.g., correct-answer checks) as the reward signal, sidestepping the need for a learned reward model on scarce tasks. ● PaLM 2 gains: Scales effectively on PaLM 2 for math and code tasks, outperforming standard SFT on human data at matched compute. ● Synthetic-data signal: A strong empirical case that self-generated filtered data can replace much of the human data bottleneck for reasoning tasks - a theme that grew through 2024.	Paper, Tweet
8) Gaussian-SLAM - A neural RGBD SLAM method that extends 3D Gaussian Splatting to achieve photorealistic scene reconstruction without sacrificing speed. ● 3D Gaussians for SLAM: Represents scenes as 3D Gaussians rather than neural fields, inheriting the fast training and rendering of Gaussian Splatting. ● Photorealistic reconstruction: Produces significantly higher-fidelity reconstructions than prior neural SLAM methods at comparable or better runtime. ● RGBD input: Uses standard RGB+depth input streams, making it compatible with off-the-shelf depth cameras for practical deployment. ● Speed/quality Pareto: Advances the Pareto frontier for RGBD SLAM, where previous methods forced a trade-off between runtime and photorealism.	Paper, Tweet
9) Pearl - Meta's Pearl is a production-ready reinforcement learning agent package designed for real-world deployment constraints. ● Production-oriented design: Built for real-world environments with limited observability, sparse feedback, and high stochasticity - conditions that usually break research-oriented RL libraries. ● Modular components: Offers modular policy networks, exploration strategies, offline RL, and safety constraints that can be composed for specific applications. ● Research + practice: Targets both researchers building new RL agents and practitioners deploying RL in production recommender systems, ranking, and control. ● Meta internal use: Reflects learnings from Meta's internal deployments, making it a rare RL library that starts from production pain rather than benchmark scores.	Paper, Tweet
10) QuIP# - Cornell's QuIP# is a 2-bit LLM quantization scheme that combines lattice codebooks with incoherence processing to close the quality gap to FP16. ● Lattice codebooks: Uses E8 lattice codebooks for weight quantization, a classical lattice-quantization technique adapted to LLM weight matrices. ● Incoherence processing: Pre-processes weight matrices to make them "incoherent" (less structured along axes), which improves lattice-quantization fidelity. ● 2-bit at 16-bit quality: Significantly closes the gap between 2-bit quantized LLMs and their unquantized 16-bit counterparts across a range of LLaMA-family models. ● Deployment impact: Makes large LLMs (e.g., Llama 2 70B) fit into consumer-grade GPU memory without catastrophic quality loss, expanding the set of models hobbyists can run locally.	Paper, Tweet

Top AI Papers of the Week (December 4 - December 10)

Paper	Links
1) Gemini 1.0 - Google launches Gemini 1.0, a multimodal family natively designed to reason across text, images, video, audio, and code from the ground up. ● Three tiers: Ships as Ultra (frontier), Pro (balanced), and Nano (on-device), covering everything from data-center reasoning to mobile inference. ● Native multimodality: Unlike "bolted-on" multimodal models, Gemini is trained multimodally from scratch, with joint tokenization across text, image, video, audio, and code. ● MMLU milestone: Gemini Ultra reports the first MMLU score above human-expert performance (90.0%), using chain-of-thought with uncertainty-weighted majority voting. ● Broad capability claims: Ultra sets SOTA on 30 of 32 benchmarks in the report, spanning multimodality, multilinguality, factuality, summarization, math/science, long-context, and reasoning.	Paper, Tweet
2) EfficientSAM - Meta's EfficientSAM is a lightweight Segment Anything variant that preserves most of SAM's zero-shot quality at a fraction of the compute. ● Masked autoencoder pretraining: Uses a SAMI (SAM-leveraged masked image) pretraining objective where a small student learns to reconstruct features aligned with the SAM teacher. ● 20x smaller and faster: Achieves roughly 20x fewer parameters and 20x faster runtime than the original SAM image encoder. ● Near-parity quality: 44.4 AP vs. 46.5 AP on zero-shot instance segmentation (within 2 points) despite the dramatic efficiency win. ● Deployment-ready: Makes SAM-grade segmentation feasible on commodity hardware, consumer devices, and real-time applications where the original SAM is too heavy.	Paper, Tweet
3) Magicoder - Magicoder is a fully open-source code LLM that closes the gap with top commercial code models at only 7B parameters via high-quality synthetic instruction data. ● OSS-Instruct data: Generates 75K synthetic instruction pairs by seeding GPT with snippets pulled from open-source code, producing more diverse and realistic training data than prior code SFT datasets. ● Broad coverage: Training data spans Python, multilingual programming, and data-science program completion, producing a genuinely general code model rather than a Python-only model. ● HumanEval+ win: MagicoderS-CL-7B (based on CodeLlama) surpasses ChatGPT on HumanEval+ with 66.5 vs. 65.9 pass@1, despite being 7B. ● Fully open: Ships with code, data, and weights, positioning Magicoder as a reproducible open baseline for instruction-tuned code generation.	Paper, Tweet
4) LLMs on Graphs - A comprehensive overview of the many ways LLMs can be applied to graph-structured data and when each pattern is useful. ● Three graph scenarios: Organizes the space by whether graphs are pure (no text), text-rich (nodes/edges carry natural language), or text-paired (graphs alongside documents). ● Three role taxonomies: Categorizes LLMs as predictors, enhancers, or aligners with GNNs - clarifying whether the LLM is the model, a feature source, or a supervisor. ● Task coverage: Spans node classification, link prediction, graph-level tasks, and reasoning over knowledge graphs. ● Open problems: Flags scalability to large graphs, handling of graph structure without loss, and integration with tool-augmented LLMs as the key unsolved directions.	Paper, Tweet
5) Llama Guard - Meta's Llama Guard is a compact, instruction-tuned safety classifier built on Llama 2-7B for input/output moderation in conversational AI. ● Llama 2-7B base: Small enough to run inline with a main generative model while handling both prompt- and response-level safety classification. ● Customizable taxonomy: The safety taxonomy is specified in the instruction prompt itself, so operators can adapt it to their use case without retraining. ● Zero-shot and few-shot: Works off the shelf for many taxonomies in zero- or few-shot mode, and can be fine-tuned on a specific policy dataset when needed. ● Open release: Ships as an open model, filling a gap for teams that want local, auditable safety classification rather than relying solely on API-side moderation.	Paper, Tweet
6) KTO (Kahneman-Tversky Optimization) - Contextual AI introduces KTO, an alignment objective derived from prospect theory that works with binary "good/bad" signals instead of preference pairs. ● Prospect-theory motivation: Models reward as a Kahneman-Tversky value function with loss aversion, replacing DPO's log-likelihood-of-preferences objective with utility maximization. ● No preference pairs needed: Works with unpaired good/bad signals, dramatically loosening data collection requirements compared to DPO or RLHF. ● Matches/beats DPO: Matches or exceeds DPO performance at model scales from 1B to 30B, a clean empirical win at similar training cost. ● Practical data advantage: Makes alignment much cheaper to run in production where paired preference data is rare but outcome feedback ("user liked/didn't like") is abundant.	Paper, Tweet
7) Chain of Code - DeepMind's Chain of Code extends CoT by encouraging LMs to write pseudocode that mixes real code with LM-simulated sub-routines. ● LMulator: The LM generates pseudocode programs and explicitly annotates sub-tasks that can't be executed; a "LMulator" simulates those sub-tasks with the LM while the interpreter handles the rest. ● Undefined-behavior handling: The interpreter catches undefined behavior and cleanly hands off to the LM, sidestepping the brittleness of code-first approaches that fail silently on hard ops. ● 84% on BIG-Bench Hard: Achieves 84% on BIG-Bench Hard - a 12-point gain over Chain of Thought and a clean demonstration that mixing exact execution with LM simulation beats either alone. ● Broad applicability: Works across math, logic, and commonsense reasoning, positioning Chain of Code as a general-purpose CoT upgrade.	Paper, Tweet
8) Data Management for LLMs - A survey of data-management research for LLM pretraining and supervised fine-tuning stages. ● Pretraining data: Covers data quantity, quality filtering, deduplication, domain composition, and curriculum strategies for large-scale pretraining. ● SFT data: Reviews instruction-data generation, quality filtering, diversity metrics, and the emerging literature on "less is more" for SFT. ● Domain and task composition: Examines how task mixing affects generalization vs. specialization in fine-tuning. ● Open challenges: Identifies dataset contamination, deduplication at trillion-token scale, and reproducible data recipes as the top open problems.	Paper, Tweet
9) RankZephyr - RankZephyr is an open-source LLM for listwise zero-shot reranking that bridges the effectiveness gap with GPT-4. ● Listwise zero-shot: Reranks a full candidate list in a single shot rather than doing pairwise or pointwise scoring, matching the paradigm GPT-4 uses most effectively. ● Open-source: Based on the open Zephyr chat model, releasing a fully reproducible stack for high-quality reranking. ● Matches/beats GPT-4: Competitive with GPT-4 on standard reranking benchmarks and outperforms GPT-4 on NovelEval, a post-training-cutoff benchmark resistant to contamination. ● Contamination-free win: The NovelEval advantage is particularly meaningful because it addresses the concern that GPT-4's strong reranking numbers are partly driven by memorization of benchmark queries.	Paper, Tweet
10) The Efficiency Spectrum of LLMs - A comprehensive review of algorithmic advancements for improving LLM efficiency across the full training-to-inference stack. ● Scaling laws and data: Covers how scaling laws and data-utilization strategies interact with efficiency - more isn't always better under compute constraints. ● Architectural innovations: Reviews attention variants, state-space models, MoE, and other architectural levers for efficient scaling. ● Training and tuning: Catalogs PEFT methods (LoRA, adapters, prefix tuning), quantization-aware training, and curriculum-based training strategies. ● Inference techniques: Surveys quantization, pruning, speculative decoding, KV-cache optimization, and batching as the inference-time efficiency toolkit.	Paper, Tweet

Top AI Papers of the Week (November 27 - December 3)

Paper	Links
1) GNoME - DeepMind's Graph Networks for Materials Exploration (GNoME) is an AI system that discovered 2.2 million new crystal structures, including 380,000 thermodynamically stable ones. ● 2.2M new crystals: Dramatically expands the known crystal inventory, with 380,000 stable materials - an order-of-magnitude leap over prior computational chemistry. ● Graph networks for stability: Predicts formation energies and stability of candidate materials using graph neural networks trained on DFT-labeled data. ● Active-learning loop: Combines exploration (proposing candidate structures) with exploitation (prioritizing high-stability candidates), iteratively expanding the frontier of known materials. ● Autonomous lab validation: A subset of predictions was validated in Berkeley's autonomous materials lab, closing the prediction-to-synthesis loop for the first time at this scale.	Paper, Tweet
2) Open-Source LLMs vs. ChatGPT - A survey cataloguing tasks where open-source LLMs claim to be on par with or better than ChatGPT. ● Task-by-task audit: Organizes claims by task category (code, math, reasoning, summarization, etc.) with the specific open models and benchmarks backing each claim. ● Gap measurement: Clarifies where open-source genuinely closes the gap vs. where "comparable" actually hides meaningful performance differences. ● Critical lens: Calls out evaluation-methodology issues in specific open-source claims, including benchmark contamination, cherry-picked subsets, and inconsistent judge setups. ● 2023 snapshot: Captures where open-source LLMs stood at the end of 2023 - a useful reference point for tracking how the gap evolved through 2024.	Paper, Tweet
3) Adversarial Diffusion Distillation (SDXL Turbo) - Stability AI's ADD trains a student diffusion model that produces high-quality images in just 1-4 sampling steps. ● Score distillation + adversarial loss: Combines score-distillation from a teacher diffusion model with an adversarial loss to maintain image fidelity in the low-step regime. ● 1-4 step generation: Produces usable images in a single step and SoTA-quality images in four, compared to 25-50 steps for typical SDXL sampling. ● Matches multi-step SoTA: Achieves image quality comparable to state-of-the-art diffusion baselines at four steps, dramatically cutting inference cost. ● Real-time generation: Enables SDXL-quality images at real-time frame rates on consumer GPUs, unlocking interactive creative tooling that was previously impractical.	Paper, Tweet
4) Seamless - Meta's Seamless is a family of models for end-to-end expressive, streaming cross-lingual speech communication. ● SeamlessExpressive: Preserves the speaker's expressive characteristics (pitch, emotion, pauses) across translation rather than flattening them into neutral speech. ● SeamlessStreaming: Produces translated speech in a streaming fashion with low latency, enabling near-real-time conversational translation. ● Low-resource coverage: An improved SeamlessM4T is trained on more low-resource language data, broadening the language coverage meaningfully beyond the original M4T. ● Safety red-teaming: Meta applies a red-teaming effort specifically for multimodal translation safety, a recognition that MT systems can amplify harmful content across languages.	Paper, Tweet
5) MEDITRON-70B - EPFL's MEDITRON is an open-source family of medical LLMs at 7B and 70B parameters, continually pretrained on curated medical corpora. ● Llama 2 base + medical pretraining: Builds on Llama 2 with continual pretraining on a curated medical corpus covering clinical papers, guidelines, and textbooks. ● Strong open medical baseline: MEDITRON-70B outperforms GPT-3.5 and Med-PaLM on standard medical QA benchmarks while being open-source. ● Close to frontier: Comes within 5% of GPT-4 and 10% of Med-PaLM 2 on MultiMedQA - competitive given the much smaller scale and open release. ● Reproducible recipe: Ships with pretraining data, code, and weights, providing a reproducible starting point for researchers and institutions building medical LLMs.	Paper, Tweet
6) Medprompt - Microsoft researchers show that careful prompt engineering can push general-purpose GPT-4 to state-of-the-art on medical benchmarks, no domain fine-tuning required. ● General-purpose prompting: Uses purely general-purpose prompt-engineering techniques (CoT, dynamic few-shot, choice-shuffling ensembling) with no medical-domain specialization. ● Medprompt recipe: Combines k-nearest-neighbor example selection, GPT-4-generated chain-of-thought rationales, and choice-shuffling to cancel answer-position biases. ● SoTA on 9 benchmarks: Achieves state-of-the-art on all nine benchmarks in MultiMedQA, beating Med-PaLM 2 and other specialized medical models. ● Broader lesson: Reopens the question of whether domain-specific pretraining is actually necessary when a frontier base model is paired with strong prompting - a framing that has recurred in later debates.	Paper, Tweet
7) UniIR - UniIR is a unified instruction-guided multimodal retriever that handles eight retrieval tasks across modalities with a single model. ● Instruction-guided: A single retriever conditioned on natural-language instructions determines which retrieval task to perform, rather than one retriever per task. ● Eight tasks: Handles image-to-text, text-to-image, composed-image retrieval, video retrieval, and other multimodal variants under one umbrella. ● Zero-shot generalization: Generalizes to unseen retrieval tasks not explicitly trained on, approaching a truly general multimodal retrieval model. ● M-BEIR benchmark: Ships with a new multimodal retrieval benchmark (M-BEIR) designed to standardize evaluation across tasks and modalities.	Paper, Tweet
8) Safe Deployment of Generative AI (Nature) - A Nature correspondence arguing that medical professionals - not commercial interests - must drive the development and deployment of generative AI in medicine. ● Privacy-first framing: Centers patient-privacy considerations as the non-negotiable constraint on medical AI deployment. ● Professional governance: Calls for clinician-led governance structures rather than commercial self-regulation, citing past failures of tech-industry oversight in regulated domains. ● Deployment guardrails: Recommends guardrails including consent, transparency of training data, and clinician accountability for AI-assisted decisions. ● Policy signal: As a Nature piece, amplifies medical-community concerns into the broader AI policy conversation at a key moment in the regulation debate.	Paper, Tweet
9) Dobb-E - NYU's Dobb-E is an affordable household-manipulation robot that learns new tasks with just 5 minutes of user demonstrations. ● 5 minutes of demos: Learns new household manipulation tasks from only ~5 minutes of demonstrations, a dramatic reduction from typical data requirements. ● Hardware design: Uses a low-cost stick-on gripper and a smartphone-driven data-collection rig, keeping the barrier to entry low for non-expert users. ● Home-specific challenges: Experiments in real homes surface challenges usually hidden in lab robotics - strong shadows, variable demo quality, and household-specific clutter. ● General-purpose household system: Positions Dobb-E as a general-purpose system for household robotics rather than a task-specific demonstrator, a step toward practical home robots.	Paper, Tweet
10) Translatotron 3 - Google's Translatotron 3 performs speech-to-speech translation using only monolingual data - no parallel corpora required. ● Fully unsupervised S2S: Learns direct speech-to-speech translation from monolingual data alone, a first for this task. ● Three-component architecture: Combines a masked autoencoder for speech representation, unsupervised embedding mapping across languages, and back-translation for alignment. ● Beats cascade baselines: Outperforms a comparable cascade of ASR + MT + TTS, a surprising result given cascade systems are typically the strong baseline. ● Paralinguistic preservation: Preserves paralinguistic features - pauses, speaking rates, and speaker identity - that cascaded systems tend to wash out in translation.	Paper, Tweet

Top AI Papers of the Week (November 20 - November 26)

Paper	Links
1) System 2 Attention (S2A) - Meta's S2A uses the LLM's own reasoning to decide what context actually matters, regenerating a clean prompt before the final response step. ● Two-pass prompting: First pass uses the LLM to filter/regenerate the input context, removing irrelevant or misleading content; second pass generates the final answer from the clean context. ● Addresses distraction: Directly targets the well-known problem that LLMs attend to irrelevant or manipulative content (e.g., opinion-laden context that biases answers). ● Factuality gains: Increases factuality on QA and reduces the model's sensitivity to biased framing or distractors inserted into the prompt. ● Math word problems: Outperforms standard attention-based LLMs on math word problems, where filtering irrelevant details is often the hard part of the task.	Paper, Tweet
2) Advancing Long-Context LLMs - A survey of methodologies for improving Transformer long-context capability across pretraining, fine-tuning, and inference stages. ● Full-stack coverage: Organizes methods by training stage - pretraining objectives, position encoding, fine-tuning recipes, and inference-time interventions. ● Position-encoding deep dive: Reviews RoPE variants, ALiBi, and other positional-encoding choices that dominate long-context extrapolation. ● Efficient attention: Catalogs sparse, linear, and memory-augmented attention mechanisms that make longer contexts tractable. ● Evaluation considerations: Addresses benchmark limitations including the "needle in a haystack" problem and the gap between nominal context length and effective usable context.	Paper, Tweet
3) Parallel Speculative Sampling - Amazon researchers propose a parallel variant of speculative sampling that achieves significant LLM inference speedups with minimal extra parameters. ● Parallel decoding: Combines speculative sampling with parallel decoding so multiple tokens can be generated and verified in a single pass. ● Tiny overhead: Requires learning only O(d_emb) additional parameters, far fewer than typical speculative-decoding draft models. ● Up to 30% speedup: Achieves up to 30% end-to-end inference speedup without compromising output quality. ● Minimal integration cost: Unlike separate-draft-model speculative decoding, this fits inside the main model with essentially no deployment overhead.	Paper, Tweet
4) Mirasol3B - Google's Mirasol3B is a multimodal model that decouples modalities into focused autoregressive components rather than forcing a single fused stream. ● Decoupled autoregressive modeling: Separates audio/video processing from text processing into focused autoregressive components that communicate through learned cross-modal interfaces. ● Handles longer videos: The decoupled design lets the model handle longer video inputs than typical end-to-end multimodal models constrained by sequence length. ● Modality-specific processing: Inputs are processed according to their modalities with appropriate tokenization rather than forcing a one-size-fits-all tokenizer. ● SoTA on video benchmarks: Outperforms prior methods on video QA, long-video QA, and audio-video-text benchmarks, validating the decoupled approach.	Paper, Tweet
5) Teaching Small LMs to Reason - An approach that teaches smaller language models to explicitly select among reasoning techniques for each problem. ● Reasoning technique menu: Trains the small LM to choose among step-by-step processing, recall-then-generate, recall-reason-generate, extract-generate, and direct-answer strategies. ● Technique selection: The model learns when to apply each strategy based on problem structure, not just which answer to produce. ● Matches 5-10x larger models: Attains zero-shot reasoning performance similar or better than models 5-10x larger on complex reasoning tasks. ● Practical scaling: Offers a recipe for teams that can't deploy frontier-scale models but need strong reasoning quality - a recurring production constraint.	Paper, Tweet
6) GPQA - A graduate-level Google-proof QA benchmark designed to stress-test reasoning in systems that might exceed human expertise. ● 448 expert questions: Consists of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. ● Google-proof by design: Questions are constructed so that even with unrestricted internet access, non-experts (~34%) perform only slightly better than random on them. ● GPT-4 gets 39%: The strongest GPT-4 baseline hits only 39% accuracy, showing a clear headroom for frontier models on expert-level reasoning. ● Scalable oversight testbed: Explicitly designed to enable scalable oversight research - experiments in supervising models whose knowledge may exceed the supervisors'.	Paper, Tweet
7) Hitchhiker's Guide From CoT to Agents - A survey mapping the conceptual evolution from chain-of-thought reasoning to modern language-agent frameworks. ● CoT foundations: Covers the mechanics underpinning CoT (few-shot prompting, self-consistency, least-to-most, tree-of-thought) with a consistent formalism. ● Mechanism theory: Explores why CoT works - in-context learning, prompt engineering theories, and emergence at scale - rather than just cataloging results. ● CoT-to-agent bridge: Traces how CoT techniques were progressively extended into tool use, multi-step planning, and full agent loops (ReAct, Reflexion, etc.). ● Framework landscape: Organizes the modern language-agent frameworks by which parts of the CoT-to-agent pipeline they emphasize, clarifying an otherwise noisy field.	Paper, Tweet
8) GAIA - Meta's GAIA is a benchmark for general AI assistants that requires reasoning, multimodal handling, web browsing, and tool use to solve real-world questions. ● Real-world questions: Questions are conceptually simple for humans but require integrated reasoning, web research, and tool use - a realistic test for assistant-style AI. ● Massive human-model gap: Humans achieve 92% accuracy while GPT-4 with plugins achieves only 15% - the widest human-AI gap on any major 2023 benchmark. ● Level-graduated difficulty: Three difficulty levels let researchers measure incremental progress rather than just binary success/failure. ● Agent-first evaluation: Explicitly designed to test AI assistants, not base LLMs - a framing that has since become dominant for agent evaluations.	Paper, Tweet
9) MedAgents - A collaborative multi-round framework for medical reasoning that uses role-playing LLM agents to improve accuracy and reasoning depth. ● Multi-agent deliberation: Multiple LLM agents take on specialist roles (e.g., different medical specialties) and deliberate in rounds over a case. ● Role-playing: Each agent has a defined role-play prompt that scopes its expertise and reasoning style, producing more diverse intermediate hypotheses. ● Consensus protocol: Agents iterate until reaching consensus or until a moderator resolves disagreements, producing a final answer with rationale. ● Reasoning gains: Improves accuracy and reasoning quality on medical QA benchmarks compared to single-agent baselines at matched compute.	Paper, Tweet
10) TÜLU 2 - Allen AI's TÜLU 2 is a suite of improved open instruction-tuned LLMs and an accompanying study of adaptation best practices. ● Open suite: Releases open models that match or exceed GPT-3.5-turbo-0301 on several benchmarks, a meaningful milestone for the open ecosystem at the time. ● Post-training recipe: The paper doubles as a practical recipe, documenting how instruction data curation, mixing ratios, and DPO-based preference training interact. ● UltraFeedback preference data: Uses UltraFeedback for preference optimization, validating that openly released preference datasets are sufficient to close much of the gap to commercial post-training pipelines. ● Adaptation research platform: Explicitly positioned as a platform for studying open adaptation techniques, informing the TÜLU 3 release that would follow in 2024.	Paper, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
7) JARVIS-1 - an open-world agent that can perceive multimodal input	Paper, Tweet
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, Tweet
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, Tweet
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, Tweet

Top AI Papers of the Week (November 6 - November 12)

Paper	Links
1) Hallucination in LLMs - a comprehensive survey	Paper, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
8) GPT4All - outlines technical details of the GPT4All model family along with the open-source repository that aims to democratize access to LLMs.	Paper, Tweet
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, Tweet
10) FreshLLMs - proposes a dynamic QA benchmark	Paper, Tweet

Top AI 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, Tweet
2) Evaluating LLMs - a comprehensive survey	Paper, Tweet
3) Battle of the Backbones - a large benchmarking framework for a diverse suite of computer vision tasks; find that while vision transformers	Paper, Tweet
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, Tweet
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, Tweet
6) Open DAC 2023 - introduces a dataset consisting of more than 38M density functional theory	Paper, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
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, Tweet
5) LLMs Meet New Knowledge - presents a benchmark to assess LLMs' abilities in knowledge understanding, differentiation, and association; benchmark results show	Paper, Tweet
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, Tweet
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, Tweet
8) CommonCanvas - a dataset of Creative-Commons-licensed	Paper, Tweet
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, Tweet
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, Tweet

Top AI 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, Tweet
2) LLMs for Software Engineering - a comprehensive survey of LLMs for software engineering, including open research and technical challenges.	Paper, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
9) AutoMix - an approach to route queries to LLMs based on the correctness of smaller language models	Paper, Tweet
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, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
4) LLMs can Learn Rules - presents a two-stage framework that learns a rule library for reasoning with LLMs; in the first stage	Paper, Tweet
5) Meta Chain-of-Thought Prompting - a generalizable chain-of-thought	Paper, Tweet
6) A Survey of LLMs for Healthcare - a comprehensive overview of LLMs applied to the healthcare domain.	Paper, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet

Top AI Papers of the Week (October 2 - October 8)

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

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, Tweet
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, Tweet
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, Tweet
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, Tweet
5) The Dawn of LMMs - a comprehensive analysis of GPT-4V to deepen the understanding of large multimodal models	Paper, Tweet
6) Training LLMs with Pause Tokens - performs training and inference on LLMs with a learnable 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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet

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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet

Top AI 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
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
7) Overview of AI Deception - provides a survey of empirical examples of AI deception.	Paper, Tweet
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, Tweet
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, Tweet
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, Tweet

Top AI 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, Tweet
2) SAM-Med2D - applies segment anything models	Paper, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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
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, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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
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, Tweet
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, Tweet
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, Tweet
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, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
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, Tweet
5) Foundation Models in Vision - presents a survey and outlook discussing open challenges and research directions for foundational models in computer vision.	Paper, Tweet
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, Tweet
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, Tweet
8) Survey of Aligned LLMs - resents a comprehensive overview of alignment approaches, including aspects like data collection, training methodologies, and model evaluation.	Paper, Tweet
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, Tweet
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, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet

Top AI 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, Tweet
2) Claude 2 - presents a detailed model card for Claude 2 along with results on a range of safety, alignment, and capabilities evaluations.	Paper, Tweet
3) Secrets of RLHF in LLMs - takes a closer look at RLHF and explores the inner workings of PPO with code included.	Paper, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet

Top AI 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, Tweet
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, Tweet
3) ClinicalGPT - a language model optimized through extensive and diverse medical data, including medical records, domain-specific knowledge, and multi-round dialogue consultations.	Paper, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet
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 , Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
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, Tweet
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 , Tweet
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 , Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
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, Tweet
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 , Tweet
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 , Tweet
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, Tweet
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, Tweet
9) LLM Research Directions - discusses a list of research directions for students looking to do research with LLMs.	Paper, Tweet
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, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
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, Tweet
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 , Tweet
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 , Tweet
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, Tweet
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, Tweet
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), Tweet
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, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
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, Tweet
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 , Tweet
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 , Tweet
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, Tweet
8) FrugalGPT - presents strategies to reduce the inference cost associated with using LLMs while improving performance.	Paper, Tweet
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, Tweet
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, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
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, Tweet
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 , Tweet
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 , Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
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, Tweet
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 , Tweet
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 , Tweet
7) DataComp: In search of the next generation of multimodal datasets - releases a new multimodal dataset benchmark containing 12.8B image-text pairs.	Paper, Tweet
8) ChatGPT for Information Extraction - provides a deeper assessment of ChatGPT's performance on the important information extraction task.	Paper, Tweet
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, Tweet
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, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
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, Tweet
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 , Tweet
6) Architectures of Topological Deep Learning: A Survey on Topological Neural Networks	Paper , Tweet
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, Tweet
8) ChatGPT: Applications, Opportunities, and Threats	Paper, Tweet
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, Tweet
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, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
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, Tweet
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 , Tweet
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 , Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet

Top AI 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, Tweet
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, Tweet
3) Eight Things to Know about Large Language Models - discusses important considerations regarding the capabilities and limitations of LLMs.	Paper, Tweet
4) A Survey of Large Language Models - a new 50 pages survey on large language models.	Paper, Tweet
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 , Tweet
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 , Tweet
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, Tweet
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, Tweet
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, Tweet
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, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
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, Tweet
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 , Tweet
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 , Tweet
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, Tweet
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, Tweet
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, Tweet
10) Machine Learning for Partial Differential Equations - Pa review examining avenues of partial differential equations research advanced by machine learning.	Paper, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
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, Tweet
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 , Tweet
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 , Tweet
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, Tweet
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, Tweet
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, Project Tweet
10) PanGu-Σ: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing - a trillion parameter language model with sparse heterogeneous computing.	Paper, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
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, Tweet
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 , Tweet
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 , Tweet
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, Tweet
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, Tweet
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, Tweet
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, Code , Tweet

Top AI 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, Demo , Tweet
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, GitHub, Project , Tweet
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, GitHub Tweet
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, Tweet
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 , Tweet
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 , Tweet
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, Code, Blog, Tweet
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, Repo, Tweet
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, Tweet
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, Project , Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
4) Consistency Models - a new family of generative models that achieve high sample quality without adversarial training.	Paper, Tweet
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 , Code, Tweet
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 , Tweet
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, Project Tweet
8) Language-Driven Representation Learning for Robotics - a framework for language-driven representation learning from human videos and captions for robotics.	Paper, Models, Evaluation, Tweet
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, Tweet
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, Tweet

Top AI 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, Tweet
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, Project , GitHub , Tweet
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, GitHub Tweet
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, Code Tweet
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 , Project, Tweet
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 , Tweet
7) Learning Performance-Improving Code Edits - an approach that uses LLMs to suggest functionally correct, performance-improving code edits.	Paper, Tweet
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, Tweet
9) Aligning Text-to-Image Models using Human Feedback - proposes a fine-tuning method to align generative models using human feedback.	Paper, Tweet
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, Tweet

Top AI 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, Tweet
2) Transformer models: an introduction and catalog	Paper, Tweet
3) 3D-aware Conditional Image Synthesis - a 3D-aware conditional generative model extended with neural radiance fields for controllable photorealistic image synthesis.	Project Tweet
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, Tweet
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
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 , Code Tweet
7) Augmented Language Models: a Survey - a survey of language models that are augmented with reasoning skills and the capability to use tools.	Paper, Tweet
8) Geometric Clifford Algebra Networks - an approach to incorporate geometry-guided transformations into neural networks using geometric algebra.	Paper, Tweet
9) Auditing large language models: a three-layered approach - proposes a policy framework for auditing LLMs.	Paper, Tweet
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, Tweet

Top AI 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, Tweet
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, Tweet
3) A Categorical Archive of ChatGPT Failures - a comprehensive analysis of ChatGPT failures for categories like reasoning, factual errors, maths, and coding.	Paper, Tweet
4) Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery - optimizing hard text prompts through efficient gradient-based optimization.	Paper, Tweet
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, Tweet
6) Structure and Content-Guided Video Synthesis with Diffusion Models - proposes an approach for structure and content-guided video synthesis with diffusion models.	Paper , Project, Tweet
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, Tweet
8) Noise2Music: Text-conditioned Music Generation with Diffusion Models - proposes diffusion models to generate high-quality 30-second music clips via text prompts.	Paper, Project, Tweet
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, Project, Tweet
10) Zero-shot Image-to-Image Translation - proposes a model for zero-shot image-to-image translation.	Paper, Project, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
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, Code Tweet
5) Dreamix: Video Diffusion Models are General Video Editors - a diffusion model that performs text-based motion and appearance editing of general videos.	Paper, Project, Tweet
6) Benchmarking Large Language Models for News Summarization	Paper , Tweet
7) Mathematical Capabilities of ChatGPT - investigates the mathematical capabilities of ChatGPT on a new holistic benchmark called GHOSTS.	Paper, Tweet
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, Project, Tweet
9) SceneDreamer: Unbounded 3D Scene Generation from 2D Image Collections - a generative model that synthesizes large-scale 3D landscapes from random noises.	Paper, Tweet
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, Tweet

Top AI 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, Tweet
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, Tweet
3) A Watermark for Large Language Models - a watermarking framework for proprietary language models.	Paper, Tweet
4) Text-To-4D Dynamic Scene Generation - a new text-to-4D model for dynamic scene generation from input text.	Paper, GitHub, Tweet
5) ClimaX: A foundation model for weather and climate - a foundation model for weather and climate, including many capabilities for atmospheric science tasks.	Paper, Tweet, Blog
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 , Tweet
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, Tweet
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, Project, Code Tweet
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, Tweet
10) The Impossibility of Parallelizing Boosting - investigates the possibility of parallelizing boosting.	Paper, Tweet

Top AI 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, Tweet
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, Tweet
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, Tweet
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, Tweet
5) Everything is Connected: Graph Neural Networks - short overview of key concepts in graph representation learning.	Paper, Tweet
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, Tweet, Project
7) InstructPix2Pix: Learning to Follow Image Editing Instructions - proposes a method with the capability of editing images from human instructions.	Paper, Tweet
8) Dataset Distillation: A Comprehensive Review	Paper, Tweet
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, Tweet
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, Tweet

Top AI 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, Tweet
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, Tweet, Code
3) Multimodal Deep Learning - multimodal deep learning is a new book published on ArXiv.	Book, Tweet
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, Tweet
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, Code, Tweet
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 , Tweet
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, Tweet
8) Scaling Laws for Generative Mixed-Modal Language Models - introduces scaling laws for generative mixed-modal language models.	Paper, Tweet
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, Tweet
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, Tweet

Top AI 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, Project, Code, Tweet
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, Tweet
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, Tweet
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, Tweet
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, Code, Tweet
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 , Code, Tweet
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, Tweet
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, Tweet
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, Code Tweet
10) A Succinct Summary of Reinforcement Learning - a nice overview of some important ideas in RL.	Paper, Tweet

We use a combination of AI-powered tools, analytics, and human curation to build the lists of papers.

Subscribe to our NLP Newsletter to stay on top of ML research and trends.

Join our Discord.

2023.md 341 KB История Исходник