In open-domain question-answering (ODQA), most existing questions require
single-hop reasoning on commonsense. To further extend this task, we officially
introduce open-domain multi-hop reasoning (ODMR) by answering multi-hop
questions with explicit reasoning steps in open-domain setting. Recently, large
language models (LLMs) have found significant utility in facilitating ODQA
without external corpus. Furthermore, chain-of-thought (CoT) prompting boosts
the reasoning capability of LLMs to a greater extent with manual or automated
paradigms. However, existing automated methods lack of quality assurance, while
manual approaches suffer from limited scalability and poor diversity, hindering
the capabilities of LLMs. In this paper, we propose Self-prompted
Chain-of-Thought (SP-CoT), an automated framework to mass-produce high quality
CoTs of LLMs, by LLMs and for LLMs. SP-CoT introduces an automated generation
pipeline of high quality ODMR datasets, an adaptive sampler for in-context CoT
selection and self-prompted inference via in-context learning. Extensive
experiments on four multi-hop question-answering benchmarks show that our
proposed SP-CoT not only significantly surpasses the previous SOTA methods on
large-scale (175B) LLMs, but also nearly doubles the zero-shot performance of
small-scale (13B) LLMs. Further analysis reveals the remarkable capability of
SP-CoT to elicit direct and concise intermediate reasoning steps by recalling
$\sim$50\% of intermediate answers on MuSiQue-Ans dataset.

在这篇文章中，我们提出了自主启发式链式思考 (SP-CoT) 自动化框架，用于大规模生成高质量的多跳推理数据集并通过上下文学习实现自主启发式推理，实验证明 SP-CoT 在大规模和小规模语言模型上的性能均显著优于前沿方法，并能在中间步骤中召回约 50% 的中间答案。