@inproceedings{jia-etal-2025-bridging,
    title = "Bridging Relevance and Reasoning: Rationale Distillation in Retrieval-Augmented Generation",
    author = "Jia, Pengyue  and
      Xu, Derong  and
      Li, Xiaopeng  and
      Du, Zhaocheng  and
      Li, Xiangyang  and
      Wang, Yichao  and
      Wang, Yuhao  and
      Liu, Qidong  and
      Wang, Maolin  and
      Guo, Huifeng  and
      Tang, Ruiming  and
      Zhao, Xiangyu",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.findings-acl.220/",
    pages = "4242--4256",
    ISBN = "979-8-89176-256-5",
    abstract = "The reranker and generator are two critical components in the Retrieval-Augmented Generation (i.e., RAG) pipeline, responsible for ranking relevant documents and generating responses. However, due to differences in pre-training data and objectives, there is an inevitable gap between the documents ranked as relevant by the reranker and those required by the generator to support answering the query. To address this gap, we propose RADIO, a novel and practical preference alignment framework with RAtionale DIstillatiOn. Specifically, We first propose a rationale extraction method that leverages the reasoning capabilities of large language models (LLMs) to extract the rationales necessary for answering the query. Subsequently, a rationale-based alignment process is designed to rerank the documents based on the extracted rationales, and fine-tune the reranker to align the preferences. We conduct extensive experiments on two tasks across three datasets to demonstrate the effectiveness of our approach compared to baseline methods. Our code is released online to ease reproduction."
}