用知识图谱减少大模型幻觉，提升医学问答可靠性
Knowledge Graph-Driven Retrieval-Augmented Generation: Integrating Deepseek-R1 with Weaviate for Advanced Chatbot Applications
- 构建AMD疾病知识图谱，融合因果关系与实体信息
- 检索增强生成使回答准确率提升，幻觉显著减少
- 适合医疗问答、临床辅助等高可靠性需求场景
大语言模型在自然语言生成中取得显著进展，但常产生未经验证的内容，影响其在关键应用中的可靠性。本文提出一种创新框架，通过检索增强生成技术将结构化生物医学知识与大模型结合。系统从与年龄相关性黄斑变性（AMD）相关的医学摘要中识别并优化因果关系与命名实体，构建全面的知识图谱。利用基于向量的检索和本地部署的语言模型，生成既上下文相关又可验证的回答，并直接引用临床证据。实验表明，该方法显著降低幻觉，提升事实准确性，改善回答清晰度，为高级生物医学聊天机器人应用提供可靠解决方案。
原文摘要 · Abstract (English)
Large language models (LLMs) have significantly advanced the field of natural language generation. However, they frequently generate unverified outputs, which compromises their reliability in critical applications. In this study, we propose an innovative framework that combines structured biomedical knowledge with LLMs through a retrieval-augmented generation technique. Our system develops a thorough knowledge graph by identifying and refining causal relationships and named entities from medical abstracts related to age-related macular degeneration (AMD). Using a vector-based retrieval process and a locally deployed language model, our framework produces responses that are both contextually relevant and verifiable, with direct references to clinical evidence. Experimental results show that this method notably decreases hallucinations, enhances factual precision, and improves the clarity of generated responses, providing a robust solution for advanced biomedical chatbot applications.
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