Summary of Towards Better Chain-of-thought: a Reflection on Effectiveness and Faithfulness, by Jiachun Li et al.
Towards Better Chain-of-Thought: A Reflection on Effectiveness and Faithfulness
by Jiachun Li, Pengfei Cao, Yubo Chen, Jiexin Xu, Huaijun Li, Xiaojian Jiang, Kang Liu, Jun Zhao
First submitted to arxiv on: 29 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 investigates the chain-of-thought (CoT) prompting technique, which has varying performance under different reasoning tasks. Previous studies have attempted to evaluate CoT but lack a deep analysis of patterns influencing its performance. This study aims to fill this gap by examining CoT effectiveness and faithfulness. The authors identify key factors affecting CoT effectiveness, including problem difficulty, information gain, and information flow. They also analyze the unfaithful CoT issue by investigating the interaction between the question, CoT, and answer. The results show that when a language model (LLM) predicts answers, it can recall correct information missing in the CoT from the question, leading to the problem. To mitigate this issue, the authors propose a novel algorithm that recalls extra information from the question to enhance CoT generation and evaluates CoTs based on their information gain. Experimental results demonstrate that this approach improves both faithfulness and effectiveness of CoT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well humans can understand each other’s thought processes when we try to generate text. We want to know why some attempts at generating text are better than others. The authors find that certain things, like the difficulty of the problem, how much new information is gained, and how smoothly information flows, affect how well this “chain-of-thought” approach works. They also figure out why sometimes our language models don’t quite understand what we’re thinking. To fix this issue, they suggest a new way to generate text that draws on more information from the question and evaluates the quality of generated text based on how much new information it includes. |
Keywords
» Artificial intelligence » Language model » Prompting » Recall
