用逻辑编程引导大模型解多跳问题，避免思维混乱。
$π$-CoT: Prolog-Initialized Chain-of-Thought Prompting for Multi-Hop Question-Answering
- 将多跳问题拆成单跳子问题，用Prolog生成逻辑路径
- 在多个基准上显著超越传统RAG和CoT方法
- 适合需要严谨推理的复杂问答场景
链式思维（CoT）提示显著提升了大语言模型的问题求解能力，但在处理复杂多跳问题时仍易陷入循环推理或偏离逻辑路径。这一局限在检索增强生成（RAG）场景中尤为明显，因正确上下文获取至关重要。本文提出一种新提示策略——$π$-CoT，结合逻辑编程的结构严谨性与语言模型的灵活性。$π$-CoT将多跳问题重构成可分解为单跳子问题的Prolog查询，依次求解并生成中间结果，再以这些结果初始化后续的链式思维推理过程。大量实验表明，$π$-CoT在多跳问答基准上显著优于标准RAG和上下文CoT方法。
原文摘要 · Abstract (English)
Chain-of-Thought (CoT) prompting significantly enhances large language models' (LLMs) problem-solving capabilities, but still struggles with complex multi-hop questions, often falling into circular reasoning patterns or deviating from the logical path entirely. This limitation is particularly acute in retrieval-augmented generation (RAG) settings, where obtaining the right context is critical. We introduce Prolog-Initialized Chain-of-Thought ($π$-CoT), a novel prompting strategy that combines logic programming's structural rigor with language models' flexibility. $π$-CoT reformulates multi-hop questions into Prolog queries decomposed as single-hop sub-queries. These are resolved sequentially, producing intermediate artifacts, with which we initialize the subsequent CoT reasoning procedure. Extensive experiments demonstrate that $π$-CoT significantly outperforms standard RAG and in-context CoT on multi-hop question-answering benchmarks.
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