论文标题
Maieutic提示：逻辑上一致的推理和递归解释
Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations
论文作者
论文摘要
尽管具有令人印象深刻的能力，但大型的预训练的语言模型（LMS）就持续的推理斗争。最近，促使LMS产生解释，即自我指导的推论已成为修改这一点的有希望的方向。但是，这些方法从根本上是由解释的正确性界定的，这些解释通常是嘈杂和不一致的。在这项工作中，我们开发了Maieutic的提示，该提示即使是从LM的嘈杂和不一致的LM来看，它也可以正确答案。 Maieutic提示诱导了绑架的解释树（例如X是真实的，因为...），然后递归地将推断置于这些解释及其逻辑关系上的令人满意的问题。我们测试了Maieutic的提示，在需要复杂的常识性推理的三个具有挑战性的基准上提示了True/false QA。 Maieutic引起的促进功能的准确性比最新的提示方法高达20％，并且作为一种完全无监督的方法，可以通过监督模型进行竞争性。我们还表明，Maieutic促使提高了推理的鲁棒性，同时提供了可解释的理由。
Despite their impressive capabilities, large pre-trained language models (LMs) struggle with consistent reasoning; recently, prompting LMs to generate explanations that self-guide the inference has emerged as a promising direction to amend this. However, these approaches are fundamentally bounded by the correctness of explanations, which themselves are often noisy and inconsistent. In this work, we develop Maieutic Prompting, which infers a correct answer to a question even from the noisy and inconsistent generations of LM. Maieutic Prompting induces a tree of explanations abductively (e.g. X is true, because ...) and recursively, then frames the inference as a satisfiability problem over these explanations and their logical relations. We test Maieutic Prompting for true/false QA on three challenging benchmarks that require complex commonsense reasoning. Maieutic Prompting achieves up to 20% better accuracy than state-of-the-art prompting methods, and as a fully unsupervised approach, performs competitively with supervised models. We also show that Maieutic Prompting improves robustness in inference while providing interpretable rationales.
