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        <title>Shifting from Ranking to Set Selection for Retrieval Augmented Generation</title>
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    <abstract>Retrieval in Retrieval-Augmented Generation (RAG) must ensure that retrieved passages are not only individually relevant but also collectively form a comprehensive set.Existing approaches primarily rerank top-k passages based on their individual relevance, often failing to meet the information needs of complex queries in multi-hop question answering.In this work, we propose a set-wise passage selection approach and introduce SetR, which explicitly identifies the information requirements of a query through Chain-of-Thought reasoning and selects an optimal set of passages that collectively satisfy those requirements.Experiments on multi-hop RAG benchmarks show that SetR outperforms both proprietary LLM-based rerankers and open-source baselines in terms of answer correctness and retrieval quality, providing an effective and efficient alternative to traditional rerankers in RAG systems.The code is available at https://github.com/LGAI-Research/SetR</abstract>
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%0 Conference Proceedings
%T Shifting from Ranking to Set Selection for Retrieval Augmented Generation
%A Lee, Dahyun
%A Jo, Yongrae
%A Park, Haeju
%A Lee, Moontae
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F lee-etal-2025-shifting
%X Retrieval in Retrieval-Augmented Generation (RAG) must ensure that retrieved passages are not only individually relevant but also collectively form a comprehensive set.Existing approaches primarily rerank top-k passages based on their individual relevance, often failing to meet the information needs of complex queries in multi-hop question answering.In this work, we propose a set-wise passage selection approach and introduce SetR, which explicitly identifies the information requirements of a query through Chain-of-Thought reasoning and selects an optimal set of passages that collectively satisfy those requirements.Experiments on multi-hop RAG benchmarks show that SetR outperforms both proprietary LLM-based rerankers and open-source baselines in terms of answer correctness and retrieval quality, providing an effective and efficient alternative to traditional rerankers in RAG systems.The code is available at https://github.com/LGAI-Research/SetR
%R 10.18653/v1/2025.acl-long.861
%U https://aclanthology.org/2025.acl-long.861/
%U https://doi.org/10.18653/v1/2025.acl-long.861
%P 17606-17619

Markdown (Informal)

[Shifting from Ranking to Set Selection for Retrieval Augmented Generation](https://aclanthology.org/2025.acl-long.861/) (Lee et al., ACL 2025)

• Shifting from Ranking to Set Selection for Retrieval Augmented Generation (Lee et al., ACL 2025)

ACL

• Dahyun Lee, Yongrae Jo, Haeju Park, and Moontae Lee. 2025. Shifting from Ranking to Set Selection for Retrieval Augmented Generation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 17606–17619, Vienna, Austria. Association for Computational Linguistics.