Large language models (LLMs) can achieve highly effective performance on various reasoning tasks by incorporating step-by-step chain-of-thought (CoT) prompting as demonstrations. However, the reasoning chains of demonstrations generated by LLMs are prone to errors, which can subsequently lead to incorrect reasoning during inference. Furthermore, inappropriate exemplars (overly simplistic or complex), can affect overall performance among varying levels of difficulty. We introduce Iter-CoT (Iterative bootstrapping in Chain-of-Thoughts Prompting), an iterative bootstrapping approach for selecting exemplars and generating reasoning chains. By utilizing iterative bootstrapping, our approach enables LLMs to autonomously rectify errors, resulting in more precise and comprehensive reasoning chains. Simultaneously, our approach selects challenging yet answerable questions accompanied by reasoning chains as exemplars with a moderate level of difficulty, which enhances the LLMs' generalizability across varying levels of difficulty. Experimental results indicate that Iter-CoT exhibits superiority, achieving competitive performance across three distinct reasoning tasks on ten datasets.

翻译：大语言模型通过引入逐步推理的思维链提示作为示例，可在各类推理任务中取得高效表现。然而，模型生成的推理链易出现错误，进而导致推理阶段的误判。此外，不当的示例（过于简单或复杂）会影响模型在不同难度层级上的整体性能。本文提出Iter-CoT（思维链提示中的迭代自举法），这是一种通过迭代自举选择示例并生成推理链的方法。通过迭代自举机制，模型能够自主修正错误，生成更精准、更完善的推理链。同时，该方法选择带有推理链、兼具挑战性与可解性的中等难度问题作为示例，从而提升模型在不同难度层级中的泛化能力。实验结果表明，Iter-CoT在三个不同推理任务的十个数据集上均展现出优越性，取得了具有竞争力的性能表现。