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 matches or outperforms standard RAG and in-context CoT on multi-hop question-answering benchmarks, with the largest gains on questions for which retrieval surfaces the necessary evidence.