Modern systems for multi-hop question answering (QA) typically break
questions into a sequence of reasoning steps, termed chain-of-thought (CoT),
before arriving at a final answer. Often, multiple chains are sampled and
aggregated through a voting mechanism over the final answers, but the
intermediate steps themselves are discarded. While such approaches improve
performance, they do not consider the relations between intermediate steps
across chains and do not provide a unified explanation for the predicted
answer. We introduce Multi-Chain Reasoning (MCR), an approach which prompts
large language models to meta-reason over multiple chains of thought, rather
than aggregating their answers. MCR examines different reasoning chains, mixes
information between them and selects the most relevant facts in generating an
explanation and predicting the answer. MCR outperforms strong baselines on 7
multi-hop QA datasets. Moreover, our analysis reveals that MCR explanations
exhibit high quality, enabling humans to verify its answers.

本研究通过 Multi-Chain Reasoning 方法，使用大型语言模型针对多个 chain-of-thought 进行 meta-reasoning，提高多跳问答的性能，并生成高质量的解释，结论基于七个多跳 QA 数据集上的测试表现优异。