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        <title>Multi-Faceted Self-Consistent Preference Alignment for Query Rewriting in Conversational Search</title>
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        <namePart type="given">Zhiyu</namePart>
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    <abstract>Conversational Query Rewriting (CQR) aims to rewrite ambiguous queries to achieve more efficient conversational search. Early studies have predominantly focused on the rewriting in isolation, ignoring the feedback from query rewrite, passage retrieval and response generation in the rewriting process. To address this issue, we propose Multi-Faceted Self-Consistent Preference Aligned CQR (MSPA-CQR). Specifically, we first construct self-consistent preference alignment data from three dimensions (rewriting, retrieval, and response) to generate more diverse rewritten queries. Then we propose prefix guided multi-faceted direct preference optimization to learn preference information from three different dimensions. The experimental results show that our MSPA-CQR is effective in both in- and out-of-distribution scenarios.</abstract>
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%0 Conference Proceedings
%T Multi-Faceted Self-Consistent Preference Alignment for Query Rewriting in Conversational Search
%A Cao, Zhiyu
%A Li, Peifeng
%A Zhu, Qiaoming
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F cao-etal-2026-multi
%X Conversational Query Rewriting (CQR) aims to rewrite ambiguous queries to achieve more efficient conversational search. Early studies have predominantly focused on the rewriting in isolation, ignoring the feedback from query rewrite, passage retrieval and response generation in the rewriting process. To address this issue, we propose Multi-Faceted Self-Consistent Preference Aligned CQR (MSPA-CQR). Specifically, we first construct self-consistent preference alignment data from three dimensions (rewriting, retrieval, and response) to generate more diverse rewritten queries. Then we propose prefix guided multi-faceted direct preference optimization to learn preference information from three different dimensions. The experimental results show that our MSPA-CQR is effective in both in- and out-of-distribution scenarios.
%U https://aclanthology.org/2026.findings-acl.638/
%P 13083-13100

Markdown (Informal)

[Multi-Faceted Self-Consistent Preference Alignment for Query Rewriting in Conversational Search](https://aclanthology.org/2026.findings-acl.638/) (Cao et al., Findings 2026)

• Multi-Faceted Self-Consistent Preference Alignment for Query Rewriting in Conversational Search (Cao et al., Findings 2026)

ACL

• Zhiyu Cao, Peifeng Li, and Qiaoming Zhu. 2026. Multi-Faceted Self-Consistent Preference Alignment for Query Rewriting in Conversational Search. In Findings of the Association for Computational Linguistics: ACL 2026, pages 13083–13100, San Diego, California, United States. Association for Computational Linguistics.