Beam Retrieval: General End-to-End Retrieval for Multi-Hop Question Answering

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.