Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models

Jiashuo Sun, Yi Luo, Yeyun Gong, Chen Lin, yelong shen, Jian Guo, Nan Duan

Keywords: Chain-of-Thought, In-context Learning, Large Language Models

Abstract: Large language models (LLMs) can achieve impressive performance on various reasoning tasks by incorporating chain-of-thought (CoT) prompting, where step-by-step reasoning is provided to guide LLMs to generate answers to questions, and the question-rationale-answer triplets are utilized as demonstration exemplars. However, the reasoning chains of demonstrations generated by LLMs are observed to be prone to errors, which can subsequently lead to incorrect reasoning during inference. Furthermore, inappropriate exemplars, e.g., overly simplistic or complex exemplars depending on the question's difficulty level, can affect the LLM's performance. To address these issues, we introduce Iter-CoT (Iterative bootstrapping in Chain-of-Thoughts prompting). Iter-CoT has two advantages: (1) it adopts iterative bootstrapping that enables LLMs to rectify errors autonomously, resulting in more precise and comprehensive reasoning chains. (2) it selects exemplars of challenging yet answerable (i.e., the LLM has the potential to answer correctly) questions, enhancing the LLMs' generalizability to answer questions with varying difficulty levels. Experimental results exhibit Iter-CoT superior performance on three distinct reasoning tasks on eleven datasets.

Primary Area: general machine learning (i.e., none of the above)

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Submission Number: 4803