用知识增强大模型，精准检索药物副作用
RAG-based Architectures for Drug Side Effect Retrieval in LLMs
- 引入RAG和GraphRAG架构，融合药物副作用知识库
- 在1.95万条关联数据上，GraphRAG实现近完美准确率
- 适合医疗AI、药物安全监测等专业领域使用
药物副作用是全球重大健康问题，亟需高效准确的检测与分析方法。尽管大语言模型（LLMs）提供了有前景的对话接口，但其依赖黑箱训练数据、易产生幻觉、缺乏领域知识等问题，限制了在药物警戒等专业领域的可靠性。为此，我们提出两种架构：检索增强生成（RAG）与GraphRAG，将全面的药物副作用知识整合进Llama 3 8B模型。在包含976种药物和3,851个不良反应术语的19,520条药物-副作用关联数据上进行评估，结果表明GraphRAG在药物副作用检索中达到近完美的准确率。该框架为大模型在关键药物警戒应用中提供了高精度且可扩展的解决方案。
原文摘要 · Abstract (English)
Drug side effects are a major global health concern, necessitating advanced methods for their accurate detection and analysis. While Large Language Models (LLMs) offer promising conversational interfaces, their inherent limitations, including reliance on black-box training data, susceptibility to hallucinations, and lack of domain-specific knowledge, hinder their reliability in specialized fields like pharmacovigilance. To address this gap, we propose two architectures: Retrieval-Augmented Generation (RAG) and GraphRAG, which integrate comprehensive drug side effect knowledge into a Llama 3 8B language model. Through extensive evaluations on 19,520 drug side effect associations (covering 976 drugs and 3,851 side effect terms), our results demonstrate that GraphRAG achieves near-perfect accuracy in drug side effect retrieval. This framework offers a highly accurate and scalable solution, signifying a significant advancement in leveraging LLMs for critical pharmacovigilance applications.
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