State of AI

Week 3, April 2023

Dear reader,

Welcome to the third edition of the State of AI newsletter! We're truly grateful for your continued support and enthusiasm for our curated selection of AI and ML research paper summaries. As the field of AI and machine learning experiences exponential growth and rapid development, it can be challenging to keep up with the latest advancements and breakthroughs.

With the vast array of progress being made, our goal is to distill the most discussed and relevant research papers of the week into a concise, digestible read for you. Our newsletter, aims to keep you informed and engaged with the most groundbreaking work in the AI and ML space.

In this week's edition, we delve into topics such as teaching large language models to self-debug, high-resolution video synthesis using latent diffusion models, emergent autonomous scientific research capabilities within large language models, the collaboration of LLMs with domain experts in OpenAGI, and the exploration of anti-aliased grid-based neural radiance fields through Zip-NeRF. We trust that these summaries will provide you with valuable insights into the cutting-edge work being done in the AI and ML space.

We hope you find these summaries insightful and valuable, and that they help you stay at the forefront of the ever-evolving AI landscape. Thank you once again for being a part of our community, and happy reading!

Best regards,

State of AI

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Contents

1. Teaching Large Language Models to Self-Debug

2. Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models

3. Emergent autonomous scientific research capabilities of large language models

4. OpenAGI: When LLM Meets Domain Experts

5. Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields

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Teaching Large Language Models to Self-Debug

Authors: Xinyun Chen, Maxwell Lin, Nathanael Schärli, Denny Zhou

Link: https://arxiv.org/abs/2304.05128

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Introduction

Large language models (LLMs) have been making impressive strides in code generation tasks such as text-to-SQL generation, code translation, and text-to-Python generation. However, generating perfect code in a single attempt remains a major challenge. The study presented in this paper aims to make LLMs more accurate and efficient in understanding and generating code by teaching them the art of SELF-DEBUGGING – an approach that enables the LLM to debug its generated code effectively.

The Art of Rubber Duck Debugging

The secret sauce of SELF-DEBUGGING is its ability to utilize rubber duck debugging. Most developers are familiar with this technique where they explain their code line-by-line to a rubber duck (or any inanimate object), which significantly improves debugging efficiency without requiring expert guidance. The idea is that by explaining the code in natural language, developers can better understand their mistakes and find ways to correct them. The authors of this study hypothesize that LLMs can also benefit from this process, and their experimental results seem to prove them right.

Achieving State-of-the-Art Performance

The authors evaluate SELF-DEBUGGING on various code generation benchmarks, including the Spider dataset for text-to-SQL generation, TransCoder for C++-to-Python translation, and MBPP for text-to-Python generation. Across all these tasks, the model consistently improves the baseline by 2-3%. Moreover, it especially excels in the hardest problems in the Spider benchmark, where it demonstrates a 9% improvement in prediction accuracy. Additionally, not only does SELF-DEBUGGING enhance coding capabilities, but it also improves sample efficiency, matching or outperforming baseline models that generate more than ten candidate programs.

Different Flavors of Feedback

The SELF-DEBUGGING technique leverages various feedback types to guide the LLM through iterative debugging steps without human supervision. These feedback types include:

1. Simple Feedback: Here, the model receives a sentence that indicates the code's correctness without going into much detail.

2. Unit Tests (UT) Feedback: In cases where unit tests are available, the model can utilize the test cases to evaluate the code, understanding whether it's working as expected or not.

3. Code Explanation (Expl.) Feedback: Similar to rubber duck debugging, the model explains the generated code in natural language, helping it identify any issues or potential improvements in the code.

Applications in Code Generation Domains

The authors showcase the effectiveness of SELF-DEBUGGING in three popular code generation domains:

1. Text-to-SQL Generation: In this task, where unit tests are not available, SELF-DEBUGGING helps the models identify errors by thoroughly explaining the predicted code. The debugging process terminates when the SQL query is considered correct, or when the maximum number of debugging turns is reached.

2. Code Translation: The goal of this task is to translate code from one programming language to another. The authors use the TransCoder dataset for their experiments, translating C++ code to Python code. The authors particularly focused on cases where the predicted Python code didn't pass all the unit tests, and SELF-DEBUGGING was used only when required.

3. Text-to-Python Generation: In this task, the model infers code correctness based on a subset of unit tests, simulating a real-world coding assignment scenario. SELF-DEBUGGING is utilized to improve the model's performance in generating the correct Python code.

Experimental Results and Findings

The experiments conducted in this research show the efficacy of SELF-DEBUGGING across different code generation tasks:

1. Text-to-SQL Generation: On the Spider benchmark, SELF-DEBUGGING with code explanation improves the baseline performance by 2-3%. Additionally, it boosts prediction accuracy for the most complex SQL queries by 9%.

2. Code Translation: On the TransCoder dataset, UT feedback and code explanation increase performance by up to 12%. Even without debugging, code explanation enhances code translation performance by 2-3%.

3. Text-to-Python Generation: SELF-DEBUGGING demonstrates improved sample efficiency compared to traditional methods. It can match or outperform baseline models, even when more than ten candidate programs are generated.

Bottom Line: Teaching Self-Debugging Helps

This study concludes that teaching LLMs to perform SELF-DEBUGGING without human guidance is a promising strategy for enhancing their coding capabilities while reducing sampling costs. By using rubber duck debugging techniques, large language models can achieve state-of-the-art performance on various code generation benchmarks and improve sample efficiency.

The SELF-DEBUGGING approach represents a significant advancement in the development of AI systems capable of generating, debugging, and self-correcting code solutions. This research brings us closer to building more efficient and robust models for a wide range of programming tasks, making it an exciting development for both AI enthusiasts and developers alike.

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High-Resolution Video Synthesis with Latent Diffusion Models

Authors: Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, Karsten Kreis

Link: https://research.nvidia.com/labs/toronto-ai/VideoLDM/

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Introduction

Machine learning advancements have made it easier to create high-quality images using generative models. However, video modeling hasn't kept up due to the significant computational cost and lack of large-scale datasets. This exciting new study on high-resolution video generation focuses on extending latent diffusion models (LDMs) to videos. The authors propose Video LDMs that efficiently transform image generators into video generators by introducing a temporal dimension to the latent space diffusion model and fine-tuning on encoded image sequences. These models target real-world applications such as driving simulations and creative text-to-video modeling.