Multi-hop QA involves finding multiple relevant passages and step-by-step
reasoning to answer complex questions. While previous approaches have developed
retrieval modules for selecting relevant passages, they face challenges in
scenarios beyond two hops, owing to the limited performance of one-step methods
and the failure of two-step methods when selecting irrelevant passages in
earlier stages. In this work, we introduce Beam Retrieval, a general end-to-end
retrieval framework for multi-hop QA. This approach maintains multiple partial
hypotheses of relevant passages at each step, expanding the search space and
reducing the risk of missing relevant passages. Moreover, Beam Retrieval
jointly optimizes an encoder and two classification heads by minimizing the
combined loss across all hops. To establish a complete QA system, we
incorporate a supervised reader or a zero-shot GPT-3.5. Experimental results
demonstrate that Beam Retrieval achieves a nearly 50% improvement compared with
baselines on challenging MuSiQue-Ans, and it also surpasses all previous
retrievers on HotpotQA and 2WikiMultiHopQA. Providing high-quality context,
Beam Retrieval helps our supervised reader achieve new state-of-the-art
performance and substantially improves (up to 28.8 points) the QA performance
of zero-shot GPT-3.5.

通过多跳问答技术以及基于 Beam Retrieval 的检索框架，本研究提出一种改进性能的问题回答方法，通过扩展搜索空间和优化模型，在多个问答任务上取得了近 50% 的性能提升，并且在 HotpotQA 和 2WikiMultiHopQA 上超过了之前的检索模型，进一步提升了有监督模型和无监督模型的回答性能。