Reprompting: Automated Chain-of-Thought Prompt Inference Through Gibbs Sampling

Weijia Xu, Andrzej Banburski, Nebojsa Jojic

Primary Area: transfer learning, meta learning, and lifelong learning

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Keywords: Prompting, In-Context Learning, Few-Shot Learning, GPT, Large Language Models, Multi-Step Reasoning, Natural Language Processing

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Abstract: We introduce Reprompting, an iterative sampling algorithm that searches for the Chain-of-Thought (CoT) recipes for a given task without human intervention. Through Gibbs sampling, we infer CoT recipes that work consistently well for a set of training samples. Our method iteratively samples new recipes using previously sampled solutions as parent prompts to solve other training problems. On five Big-Bench Hard tasks that require multi-step reasoning, Reprompting achieves consistently better performance than the zero-shot, few-shot, human-written CoT, Auto-CoT and self-consistency decoding baselines. Reprompting can also facilitate transfer of knowledge from a stronger model to a weaker model leading to substantially improved performance of the weaker model. Overall, Reprompting brings up to +17 point improvements over the previous state-of-the-art method that uses human-written CoT prompts.

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Submission Number: 9008