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Despite their impressive capabilities, large pre-trained language models(LMs) struggle with consistent reasoning; recently, prompting LMs to generateexplanations that self-guide the inference has emerged as a promising directionto amend this. However, these approaches are fundamentally bounded by thecorrectness of explanations, which themselves are often noisy and inconsistent.In this work, we develop Maieutic Prompting, which infers a correct answer to aquestion even from the noisy and inconsistent generations of LM. MaieuticPrompting induces a tree of explanations abductively (e.g. X is true, because...) and recursively, then frames the inference as a satisfiability problemover these explanations and their logical relations. We test Maieutic Promptingfor true/false QA on three challenging benchmarks that require complexcommonsense reasoning. Maieutic Prompting achieves up to 20% better accuracythan state-of-the-art prompting methods, and as a fully unsupervised approach,performs competitively with supervised models. We also show that MaieuticPrompting improves robustness in inference while providing interpretablerationales.

Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations