Paper | Rationale-Guided Retrieval Augmented Generation for Medical Question Answering
Authors: Jiwoong Sohn, Yein Park, Chanwoong Yoon, Sihyeon Park, Hyeon Hwang, Mujeen Sung, Hyunjae Kim, Jaewoo Kang
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models (LLM) hold significant potential for applications in biomedicine, but they struggle with hallucinations and outdated knowledge.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 incorrect 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 RAG² (RAtionale-Guided RAG), a new framework for enhancing the reliability of RAG in biomedical contexts. RAG² 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 RAG² 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.
Code | Data and code will be available soon.
If you use this work, please cite our paper:
@article{sohn2024rag,
title={Rationale-Guided Retrieval Augmented Generation for Medical Question Answering},
author={Jiwoong Sohn and Yein Park and Chanwoong Yoon and Sihyeon Park and Hyeon Hwang and Mujeen Sung and Hyunjae Kim and Jaewoo Kang},
journal={arXiv preprint arXiv:2411.00300},
year={2024}
}
Stay tuned for updates on data and code!