通过查证-重查机制，让检索系统更全面地找到多样答案。
RVR: Retrieve-Verify-Retrieve for Comprehensive Question Answering
- 三轮检索：先找文档，再验证好内容，最后用已验证信息扩检遗漏答案。
- 在QAMPARI数据集上，完整召回率提升至少10%相对、3%绝对。
- 适用于多种检索器，尤其适合需要全面回答的复杂问题场景。
全面检索多样文档对于回答存在多种有效答案的问题至关重要。本文提出检索-验证-再检索（RVR）框架，通过多轮迭代最大化答案覆盖率。初始阶段，检索器基于原始查询返回候选文档集，随后验证器筛选出高质量子集；后续轮次中，将先前验证过的文档融入查询，以发现尚未覆盖的答案。RVR在使用现成检索器时即表现良好，对检索器进行微调可进一步提升性能。该方法在多答案检索数据集QAMPARI上超越基线模型，完整召回率实现至少10%相对提升和3%绝对提升；同时在两个跨域数据集（QUEST和WebQuestionsSP）上也表现出一致增益，且适用于不同基础检索器。本工作展示了通过验证机制与检索器适配新推理流程的高效迭代策略。
原文摘要 · Abstract (English)
Comprehensively retrieving diverse documents is crucial to address queries that admit a wide range of valid answers. We introduce retrieve-verify-retrieve (RVR), a multi-round retrieval framework designed to maximize answer coverage. Initially, a retriever takes the original query and returns a candidate document set, followed by a verifier that identifies a high-quality subset. For subsequent rounds, the query is augmented with previously verified documents to uncover answers that are not yet covered in previous rounds. RVR is effective even with off-the-shelf retrievers, and fine-tuning retrievers for our inference procedure brings further gains. Our method outperforms baselines, including agentic search approaches, achieving at least 10% relative and 3% absolute gain in complete recall percentage on a multi-answer retrieval dataset (QAMPARI). We also see consistent gains on two out-of-domain datasets (QUEST and WebQuestionsSP) across different base retrievers. Our work presents a promising iterative approach for comprehensive answer recall leveraging a verifier and adapting retrievers to a new inference scenario.
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