@inproceedings{sohn-etal-2025-rationale,
    title = "Rationale-Guided Retrieval Augmented Generation for Medical Question Answering",
    author = "Sohn, Jiwoong  and
      Park, Yein  and
      Yoon, Chanwoong  and
      Park, Sihyeon  and
      Hwang, Hyeon  and
      Sung, Mujeen  and
      Kim, Hyunjae  and
      Kang, Jaewoo",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-long.635/",
    pages = "12739--12753",
    ISBN = "979-8-89176-189-6",
    abstract = "Large language models (LLM) hold significant potential for applications in biomedicine, but they struggle with hallucinations and outdated knowledge.While retrieval-augmented generation (RAG) is generally employed to address these issues, it also has its own set of challenges: (1) LLMs are vulnerable to irrelevant or unhelpful context, (2) medical queries are often not well-targeted for helpful information, and (3) retrievers are prone to bias toward the specific source corpus they were trained on. In this study, we present RAG$^2$ (RAtionale-Guided RAG), a new framework for enhancing the reliability of RAG in biomedical contexts. RAG$^2$ incorporates three key innovations: a small filtering model trained on perplexity-based labels of rationales, which selectively augments informative snippets of documents while filtering out distractors; LLM-generated rationales as queries to improve the utility of retrieved snippets; a structure designed to retrieve snippets evenly from a comprehensive set of four biomedical corpora, effectively mitigating retriever bias. Our experiments demonstrate that RAG$^2$ improves the state-of-the-art LLMs of varying sizes, with improvements of up to 6.1{\%}, and it outperforms the previous best medical RAG model by up to 5.6{\%} across three medical question-answering benchmarks. Our code is available at https://github.com/dmis-lab/RAG2"
}