RVR: Retrieve-Verify-Retrieve for Comprehensive Question Answering
arXiv:2602.18425v1 Announce Type: new Abstract: 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
arXiv:2602.18425v1 Announce Type: new Abstract: 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.
Executive Summary
This article introduces the Retrieve-Verify-Retrieve (RVR) framework, a multi-round retrieval method designed to maximize answer coverage in comprehensive question answering. The framework consists of a retriever, verifier, and subsequent retrievals, with the query augmented with previously verified documents. The RVR method outperforms baselines on various datasets, achieving significant gains in complete recall percentage. While the method shows promise, its reliance on off-the-shelf retrievers and fine-tuning procedures may limit its scalability and generalizability. The article highlights the importance of iterative approaches in comprehensive answer recall, leveraging verifiers and adapting retrievers to new inference scenarios. The RVR framework has the potential to improve the accuracy and completeness of question answering systems, but further research is needed to fully realize its potential.
Key Points
- ▸ The RVR framework introduces a multi-round retrieval method for comprehensive question answering.
- ▸ The framework consists of a retriever, verifier, and subsequent retrievals with query augmentation.
- ▸ RVR outperforms baselines on various datasets, achieving significant gains in complete recall percentage.
Merits
Strength in iterative approach
The RVR framework's iterative approach allows for the adaptation of retrievers to new inference scenarios, improving answer coverage and recall.
Demerits
Limited scalability and generalizability
RVR's reliance on off-the-shelf retrievers and fine-tuning procedures may limit its scalability and generalizability to diverse datasets and domains.
Expert Commentary
The RVR framework's iterative approach and use of verifiers and retriever adaptation are innovative and promising. However, further research is needed to fully realize the framework's potential and address its limitations. Additionally, the article's findings on the importance of iterative approaches may have broader implications for the development of question answering systems and policies. The RVR framework has the potential to improve the accuracy and completeness of question answering systems, but its effectiveness in real-world applications will depend on its scalability and generalizability.
Recommendations
- ✓ Further research is needed to explore the RVR framework's limitations and potential scalability and generalizability issues.
- ✓ The development of more effective question answering policies and guidelines that incorporate iterative approaches and verifiers may be warranted.
