@inproceedings{kim-etal-2026-relevance,
    title = "Relevance to Utility: Process-Supervised Rewrite for {RAG}",
    author = "Kim, Jaeyoung  and
      Kim, Jongho  and
      Hwang, Seung-won  and
      Song, Seoho  and
      Song, Young-In",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1513/",
    pages = "30274--30293",
    ISBN = "979-8-89176-395-1",
    abstract = "Retrieval-augmented generation systems often suffer from a gap between optimizing retrieval relevance and generative utility. With such a gap, retrieved documents may be topically relevant but still lack the content needed for effective reasoning during generation. While existing bridge modules attempt to rewrite the retrieved text for better generation, we show how they fail by not capturing ``document utility''. In this work, we propose R2U, with a key distinction of approximating true utility through joint observation of rewriting and answering in the reasoning process. To distill this observation reliably, R2U scales such supervision to enhance reliability in distillation. We further construct utility-improvement supervision by measuring the generator{'}s gain of the answer under the rewritten context, yielding signals for fine-tuning and preference optimization. We evaluate our method across multiple open-domain question-answering benchmarks. The empirical results demonstrate consistent improvements over strong bridging baselines."
}