Combining different forms of prompts with pre-trained large language models
has yielded remarkable results on reasoning tasks (e.g. Chain-of-Thought
prompting). However, along with testing on more complex reasoning, these
methods also expose problems such as invalid reasoning and fictional reasoning
paths. In this paper, we develop \textit{Hypothesis Testing Prompting}, which
adds conclusion assumptions, backward reasoning, and fact verification during
intermediate reasoning steps. \textit{Hypothesis Testing prompting} involves
multiple assumptions and reverses validation of conclusions leading to its
unique correct answer. Experiments on two challenging deductive reasoning
datasets ProofWriter and RuleTaker show that hypothesis testing prompting not
only significantly improves the effect, but also generates a more reasonable
and standardized reasoning process.

本文介绍了使用不同形式的提示与预训练的大型语言模型相结合，在推理任务中取得了卓越的成果（如思路链提示）。然而，除了在更复杂的推理上进行测试外，这些方法还暴露出无效推理和虚构推理路径等问题。本文开发了一种名为 “假设测试提示” 的方法，在中间推理步骤中添加了结论假设、逆向推理和事实验证。假设测试提示涉及多个假设，并反向验证导致其独特正确答案的结论。在两个具有挑战性的演绎推理数据集 ProofWriter 和 RuleTaker 上的实验证明，假设测试提示不仅显著提高了效果，还生成了一个更合理和标准化的推理过程。