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    <abstract>Retrieval-augmented generation (RAG) is usually integrated into large language models (LLMs) to mitigate hallucinations and knowledge obsolescence. Whereas, conventional one-step retrieve-and-read methods are insufficient for multi-hop question answering, facing challenges of retrieval semantic mismatching and the high cost in handling interdependent subquestions. In this paper, we propose Optimizing Question Semantic Space for Dynamic Retrieval-Augmented Multi-hop Question Answering (Q-DREAM). Q-DREAM consists of three key modules: (1) the Question Decomposition Module (QDM), which decomposes multi-hop questions into fine-grained subquestions; (2) the Subquestion Dependency Optimizer Module (SDOM), which models the interdependent relations of subquestions for better understanding; and (3) the Dynamic Passage Retrieval Module (DPRM), which aligns subquestions with relevant passages by optimizing the semantic embeddings.Experimental results across various benchmarks demonstrate that Q-DREAM significantly outperforms existing RAG methods, achieving state-of-the-art performance in both in-domain and out-of-domain settings. Notably, Q-DREAM also improves retrieval efficiency while maintaining high accuracy compared with recent baselines.</abstract>
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%0 Conference Proceedings
%T Optimizing Question Semantic Space for Dynamic Retrieval-Augmented Multi-hop Question Answering
%A Ye, Linhao
%A Yu, Lang
%A Lei, Zhikai
%A Chen, Qin
%A Zhou, Jie
%A He, Liang
%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 ye-etal-2025-optimizing
%X Retrieval-augmented generation (RAG) is usually integrated into large language models (LLMs) to mitigate hallucinations and knowledge obsolescence. Whereas, conventional one-step retrieve-and-read methods are insufficient for multi-hop question answering, facing challenges of retrieval semantic mismatching and the high cost in handling interdependent subquestions. In this paper, we propose Optimizing Question Semantic Space for Dynamic Retrieval-Augmented Multi-hop Question Answering (Q-DREAM). Q-DREAM consists of three key modules: (1) the Question Decomposition Module (QDM), which decomposes multi-hop questions into fine-grained subquestions; (2) the Subquestion Dependency Optimizer Module (SDOM), which models the interdependent relations of subquestions for better understanding; and (3) the Dynamic Passage Retrieval Module (DPRM), which aligns subquestions with relevant passages by optimizing the semantic embeddings.Experimental results across various benchmarks demonstrate that Q-DREAM significantly outperforms existing RAG methods, achieving state-of-the-art performance in both in-domain and out-of-domain settings. Notably, Q-DREAM also improves retrieval efficiency while maintaining high accuracy compared with recent baselines.
%R 10.18653/v1/2025.acl-long.871
%U https://aclanthology.org/2025.acl-long.871/
%U https://doi.org/10.18653/v1/2025.acl-long.871
%P 17814-17824

Markdown (Informal)

[Optimizing Question Semantic Space for Dynamic Retrieval-Augmented Multi-hop Question Answering](https://aclanthology.org/2025.acl-long.871/) (Ye et al., ACL 2025)

• Optimizing Question Semantic Space for Dynamic Retrieval-Augmented Multi-hop Question Answering (Ye et al., ACL 2025)

ACL

• Linhao Ye, Lang Yu, Zhikai Lei, Qin Chen, Jie Zhou, and Liang He. 2025. Optimizing Question Semantic Space for Dynamic Retrieval-Augmented Multi-hop Question Answering. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 17814–17824, Vienna, Austria. Association for Computational Linguistics.