让AI自动推理更一致，减少错误。
Self-Harmonized Chain of Thought
- 自动生成多种解题思路并迭代优化统一
- 在数学等任务上平均比现有方法高2.8%
- 适合需要可靠自动推理的研究者
链式思维提示（CoT）使大语言模型能通过中间步骤完成复杂推理。然而当前方法存在局限：零样本CoT易出错，少样本CoT需人工设计示范；Auto-CoT虽可自动生成多样示范，但导致推理模式不一致。本文提出ECHO（Self-Harmonized Chain of Thought），通过迭代过程对自动生成的示范进行精炼与调和，形成一致有效的推理路径。在算术、常识与符号推理任务上的全面实验表明，ECHO平均优于Auto-CoT 2.8%。结果表明，ECHO为大模型实现更鲁棒、泛化的自动推理迈出了重要一步。
原文摘要 · Abstract (English)
Chain-of-thought (CoT) prompting has demonstrated the capacity of large language models to perform complex reasoning through intermediate steps. While effective, current CoT methods face challenges: Zero-shot-CoT can lead to reasoning errors, and Few-shot-CoT requires labor-intensive manual demonstrations. Auto-CoT attempts to address these issues by automatically generating diverse demonstrations, but this diversity can lead to inconsistent reasoning patterns. We propose ECHO (Self-Harmonized Chain of Thought), a novel method that unifies diverse solution paths into a consistent and effective reasoning pattern. ECHO employs an iterative process to refine and harmonize automatically generated demonstrations, mitigating the limitations of existing approaches. Our comprehensive experiments across arithmetic, commonsense, and symbolic reasoning tasks demonstrate that ECHO outperforms Auto-CoT by an average of 2.8%. These findings suggest that ECHO represents a significant step towards more robust and generalizable automated reasoning in large language models.
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