思维链的理论理解：连贯推理与错误感知演示
A Theoretical Understanding of Chain-of-Thought: Coherent Reasoning and Error-Aware Demonstration
AI总结：
本文从理论上证明连贯思维链（Coherent CoT）相比逐步上下文学习具有更好的纠错能力，并发现Transformer对中间推理步骤错误更敏感，据此提出在演示中同时纳入正确与错误推理路径的改进方法并实验验证其有效性。
摘要：
少样本思维链（CoT）提示在提升大语言模型（LLMs）推理能力方面展现出强大性能。尽管已有理论研究试图理解CoT，但这些研究中所使用的底层Transformer将CoT推理过程隔离为分离的上下文学习步骤（Stepwise ICL）。在本工作中，我们从理论上证明，与Stepwise ICL相比，如果整合来自早期步骤的推理（Coherent CoT），Transformer将获得更好的纠错能力和更准确的预测。鉴于这种连贯推理改变了Transformer的行为，我们进一步研究了当演示示例在推理阶段被破坏时，Coherent CoT下Transformer的敏感性。我们的理论结果表明，Transformer对中间推理步骤中的错误比对最终结果中的错误更为敏感。基于这一观察，我们提出了一种CoT改进方法，在演示中同时纳入正确和错误的推理路径。我们的实验验证了所提方法的有效性。
英文摘要：
Few-shot Chain-of-Thought (CoT) prompting has demonstrated strong performance in improving the reasoning capabilities of large language models (LLMs). While theoretical investigations have been conducted to understand CoT, the underlying transformer used in these studies isolates the CoT reasoning process into separated in-context learning steps (Stepwise ICL). In this work, we theoretically show that, compared to Stepwise ICL, the transformer gains better error correction ability and more accurate predictions if the reasoning from earlier steps (Coherent CoT) is integrated. Given that this coherent reasoning changes the behavior of the transformer, we further investigate the sensitivity of the transformer with Coherent CoT when the demonstration examples are corrupted at the inference stage. Our theoretical results indicate that the transformer is more sensitive to errors in intermediate reasoning steps than the final outcome. Building upon this observation, we propose an improvement on CoT by incorporating both correct and incorrect reasoning paths in the demonstration. Our experiments validate the effectiveness of the proposed approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
