Summary of Understanding Chain-of-thought in Llms Through Information Theory, by Jean-francois Ton et al.
Understanding Chain-of-Thought in LLMs through Information Theory
by Jean-Francois Ton, Muhammad Faaiz Taufiq, Yang Liu
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel framework to formalize Chain-of-Thought (CoT) reasoning in Large Language Models (LLMs), enabling the quantification of information gain at each reasoning step. This approach addresses the limitations of existing CoT evaluation techniques, which either require annotated data or fall short in assessing intermediate reasoning steps. The proposed framework is demonstrated to be effective through extensive experiments on toy and GSM-8K data, outperforming existing outcome-based methods by providing more accurate insights into model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super smart computers that can solve hard problems. They do this by breaking down big questions into smaller steps. But right now, it’s hard to know if they’re doing the right thing or not. This paper figures out a way to measure how well LLMs are thinking step-by-step. It does this by looking at how much information is being added at each step. This helps us understand where the models might be going wrong. The researchers tested their new approach on some computer programs and showed that it works better than other methods. |
