RGAR通过融合病历与文献知识，提升医疗问答的准确性。
RGAR: Recurrence Generation-augmented Retrieval for Factual-aware Medical Question Answering
- 双源检索：从电子病历和医学语料库中同时获取事实与概念知识
- 在三个医疗问答基准上超越现有方法，小模型性能超过大模型GPT-3.5
- 适合临床决策支持系统，尤其关注真实病历数据的精准问答
医疗问答需要广泛调用专业概念知识。当前主流的检索增强生成（RAG）通过大规模语料库检索获取医学知识，并用以指导通用大语言模型生成答案。然而，现有检索方法常忽视事实性知识的重要性，导致检索到的概念知识相关性不足，限制了其在临床决策等实际场景中的应用，如基于电子健康记录（EHRs）的诊断支持。本文提出RGAR——一种复发生成增强检索框架，可从双重来源（即EHRs与语料库）中检索相关事实与概念知识，并使二者交互优化。在三个事实感知型医疗问答基准上的广泛评估表明，RGAR在医疗RAG系统中达到新最优性能。值得注意的是，使用RGAR的Llama-3.1-8B-Instruct模型表现优于更大规模的、经RAG增强的GPT-3.5。研究结果证明，提取事实知识用于检索能持续提升生成质量。
原文摘要 · Abstract (English)
Medical question answering requires extensive access to specialized conceptual knowledge. The current paradigm, Retrieval-Augmented Generation (RAG), acquires expertise medical knowledge through large-scale corpus retrieval and uses this knowledge to guide a general-purpose large language model (LLM) for generating answers. However, existing retrieval approaches often overlook the importance of factual knowledge, which limits the relevance of retrieved conceptual knowledge and restricts its applicability in real-world scenarios, such as clinical decision-making based on Electronic Health Records (EHRs). This paper introduces RGAR, a recurrence generation-augmented retrieval framework that retrieves both relevant factual and conceptual knowledge from dual sources (i.e., EHRs and the corpus), allowing them to interact and refine each another. Through extensive evaluation across three factual-aware medical question answering benchmarks, RGAR establishes a new state-of-the-art performance among medical RAG systems. Notably, the Llama-3.1-8B-Instruct model with RGAR surpasses the considerably larger, RAG-enhanced GPT-3.5. Our findings demonstrate the benefit of extracting factual knowledge for retrieval, which consistently yields improved generation quality.
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