Update README.md

README.md

@@ -3,13 +3,13 @@
 
 | **Paper / Project**  | **Link** |
 | ------------- |      :---:       |
-| 1. GoogleAI introduces Muse, a new text-to-image generation model based on masked generative transformers; significantly more efficient than other diffusion models like Imagen and DALLE-2.  | [Paper](https://arxiv.org/abs/2301.00704) [Project](https://muse-model.github.io/)|
-| 2. Microsoft introduces VALL-E, a text-to-audio model that performs state-of-the-art zero-shot performance; the text-to-speech synthesis task is treated as a conditional language modeling task:  | [Project](https://valle-demo.github.io/) |
-| 3. A new paper shows the potential of enhancing LLMs by retrieving relevant external knowledge based on decomposed reasoning steps obtained through chain-of-thought prompting.  | [Paper](https://arxiv.org/abs/2301.00303) |
-| 4. Presents a technique for compressing large language models while not sacrificing performance; "pruned to at least 50% sparsity in one-shot, without any retraining."  | [Paper](https://arxiv.org/pdf/2301.00774.pdf)  |
-| 5. ConvNeXt V2 is a performant model based on a fully convolutional masked autoencoder framework and other architectural improvements. CNNs are sticking back!  | [Paper](https://arxiv.org/abs/2301.00808)  |
-| 6. With more capabilities, we are starting to see a wider range of applications with LLMs. This paper utilized large language models for conducting corporate lobbying activities.  | [Paper](https://arxiv.org/abs/2301.01181)  |
-| 7. This work aims to better understand how deep learning models overfit or memorize examples; interesting phenomena observed; important work toward a mechanistic theory of memorization.  | [Paper](https://transformer-circuits.pub/2023/toy-double-descent/index.html)  |
-| 8. StitchNet: Interesting idea to create new coherent neural networks by reusing pretrained fragments of existing NNs. Not straightforward but there is potential in terms of efficiently reusing learned knowledge in pre-trained networks for complex tasks.  | [Paper](https://arxiv.org/abs/2301.01947)  |
-| 9. Proposes integrated decomposition, an approach to improve Science Q&A through a human-in-the-loop workflow for refining compositional LM programs.  | [Paper](https://arxiv.org/abs/2301.01751)  |
-| 10. A Succinct Summary of Reinforcement Learning. A nice little overview of some important ideas in RL.  | [Paper](https://arxiv.org/abs/2301.01379)  |
+| 1. **Muse: Text-To-Image Generation via Masked Generative Transformers** GoogleAI introduces Muse, a new text-to-image generation model based on masked generative transformers; significantly more efficient than other diffusion models like Imagen and DALLE-2.  | [Paper](https://arxiv.org/abs/2301.00704) [Project](https://muse-model.github.io/)|
+| 2. **VALL-E Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers** Microsoft introduces VALL-E, a text-to-audio model that performs state-of-the-art zero-shot performance; the text-to-speech synthesis task is treated as a conditional language modeling task:  | [Project](https://valle-demo.github.io/) |
+| 3. **Rethinking with Retrieval: Faithful Large Language Model Inference** A new paper shows the potential of enhancing LLMs by retrieving relevant external knowledge based on decomposed reasoning steps obtained through chain-of-thought prompting.  | [Paper](https://arxiv.org/abs/2301.00303) |
+| 4. **SPARSEGPT: Massive Language Models Can Be Accurately Pruned In One-Shot** Presents a technique for compressing large language models while not sacrificing performance; "pruned to at least 50% sparsity in one-shot, without any retraining."  | [Paper](https://arxiv.org/pdf/2301.00774.pdf)  |
+| 5. **ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders** ConvNeXt V2 is a performant model based on a fully convolutional masked autoencoder framework and other architectural improvements. CNNs are sticking back!  | [Paper](https://arxiv.org/abs/2301.00808)  |
+| 6. **Large Language Models as Corporate Lobbyists** With more capabilities, we are starting to see a wider range of applications with LLMs. This paper utilized large language models for conducting corporate lobbying activities.  | [Paper](https://arxiv.org/abs/2301.01181) [Paper/Code/Data](https://github.com/JohnNay/llm-lobbyist)  |
+| 7. **Superposition, Memorization, and Double Descent** This work aims to better understand how deep learning models overfit or memorize examples; interesting phenomena observed; important work toward a mechanistic theory of memorization.  | [Paper](https://transformer-circuits.pub/2023/toy-double-descent/index.html)  |
+| 8. **StitchNet: Composing Neural Networks from Pre-Trained Fragments** StitchNet: Interesting idea to create new coherent neural networks by reusing pretrained fragments of existing NNs. Not straightforward but there is potential in terms of efficiently reusing learned knowledge in pre-trained networks for complex tasks.  | [Paper](https://arxiv.org/abs/2301.01947)  |
+| 9. **Iterated Decomposition: Improving Science Q&A by Supervising Reasoning Processes** Proposes integrated decomposition, an approach to improve Science Q&A through a human-in-the-loop workflow for refining compositional LM programs.  | [Paper](https://arxiv.org/abs/2301.01751)  |
+| 10. **A Succinct Summary of Reinforcement Learning** A nice little overview of some important ideas in RL.  | [Paper](https://arxiv.org/abs/2301.01379)  |