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    <abstract>Retrieval-Augmented Generation (RAG) enhances the factuality of Large Language Models (LLMs) by incorporating retrieved documents and/or generated context. However, LLMs often exhibit a stylistic bias when presented with mixed contexts, favoring fluent but hallucinated generated content over factually grounded yet disorganized retrieved evidence. This phenomenon reveals that the utility of retrieved information is bottlenecked by its presentation. To bridge this gap, we propose QREAM, a style-controlled rewriter that aligns retrieved documents with a question-oriented style while preserving facts, better for LLM readers to utilize. Our framework consists of two stages: (1) QREAM-ICL, which uses stylistic seeds to guide iterative rewriting exploration; and (2) QREAM-FT, a lightweight student model distilled from denoised ICL outputs. QREAM-FT employs dual-criteria rejection sampling, filtering based on answer correctness and factual consistency to ensure high-quality supervision. QREAM seamlessly integrates into existing RAG pipelines as a plug-and-play module. Experiments demonstrate that QREAM consistently enhances advanced RAG pipelines, yielding up to 8% relative improvement with negligible latency overhead, effectively balancing question relevance with factual grounding.</abstract>
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%0 Conference Proceedings
%T Align Documents to Questions: Question-Oriented Document Rewriting for Retrieval-Augmented Generation
%A Li, Jiaang
%A Mao, Zhendong
%A Wang, Quan
%A Wan, Yuning
%A Zhang, Yongdong
%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 li-etal-2026-align
%X Retrieval-Augmented Generation (RAG) enhances the factuality of Large Language Models (LLMs) by incorporating retrieved documents and/or generated context. However, LLMs often exhibit a stylistic bias when presented with mixed contexts, favoring fluent but hallucinated generated content over factually grounded yet disorganized retrieved evidence. This phenomenon reveals that the utility of retrieved information is bottlenecked by its presentation. To bridge this gap, we propose QREAM, a style-controlled rewriter that aligns retrieved documents with a question-oriented style while preserving facts, better for LLM readers to utilize. Our framework consists of two stages: (1) QREAM-ICL, which uses stylistic seeds to guide iterative rewriting exploration; and (2) QREAM-FT, a lightweight student model distilled from denoised ICL outputs. QREAM-FT employs dual-criteria rejection sampling, filtering based on answer correctness and factual consistency to ensure high-quality supervision. QREAM seamlessly integrates into existing RAG pipelines as a plug-and-play module. Experiments demonstrate that QREAM consistently enhances advanced RAG pipelines, yielding up to 8% relative improvement with negligible latency overhead, effectively balancing question relevance with factual grounding.
%U https://aclanthology.org/2026.findings-acl.884/
%P 17838-17849

Markdown (Informal)

[Align Documents to Questions: Question-Oriented Document Rewriting for Retrieval-Augmented Generation](https://aclanthology.org/2026.findings-acl.884/) (Li et al., Findings 2026)

• Align Documents to Questions: Question-Oriented Document Rewriting for Retrieval-Augmented Generation (Li et al., Findings 2026)

ACL

• Jiaang Li, Zhendong Mao, Quan Wang, Yuning Wan, and Yongdong Zhang. 2026. Align Documents to Questions: Question-Oriented Document Rewriting for Retrieval-Augmented Generation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 17838–17849, San Diego, California, United States. Association for Computational Linguistics.