自协调思维链
Self-Harmonized Chain of Thought
AI总结：
针对现有思维链（CoT）方法存在的推理错误、人工依赖或推理模式不一致等问题，提出ECHO方法，通过迭代优化协调自动生成的演示，统一多样解决路径为一致推理模式，在多任务上平均性能优于Auto-CoT 2.8%。
摘要：
思维链（CoT）提示已证明大型语言模型可通过中间步骤执行复杂推理。尽管有效，当前CoT方法仍面临挑战：零样本CoT易导致推理错误，少样本CoT需人工构建演示，耗时费力。Auto-CoT试图通过自动生成多样演示解决这些问题，但多样性可能引发推理模式不一致。我们提出ECHO（自协调思维链），一种将多样解决路径统一为一致有效推理模式的新方法。ECHO采用迭代过程优化并协调自动生成的演示，缓解现有方法的局限性。我们在算术、常识和符号推理任务中的综合实验表明，ECHO在平均水平上比Auto-CoT高出2.8%。这些发现表明ECHO为大型语言模型中更稳健且可泛化的自动推理迈出了重要一步。
英文摘要：
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.
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。
