让医疗对话更准更贴心，自动选对知识和范例
Enhancing Medical Dialogue Generation through Knowledge Refinement and Dynamic Prompt Adjustment
- 用过滤机制剔除无关医学信息，提升关键实体识别
- 在两个数据集上生成质量与医学准确性均超越现有方法
- 适合需要高精度医疗对话的临床辅助系统开发者
医疗对话系统（MDS）作为支持多轮、上下文感知医患交流的关键在线平台，仍面临两大挑战：难以准确识别相关医学知识，以及生成个性化且医学正确的回应。为此，我们提出MedRef，一种融合知识精炼与动态提示调整的新型医疗对话系统。首先，通过知识精炼机制过滤无关医学数据，提升响应中关键医学实体的预测准确性；其次，设计包含历史信息与明显特征的综合提示结构。为实现对不同患者状况的实时适应，系统引入双模块——三元组过滤器（Triplet Filter）与示范选择器（Demo Selector），动态提供合适知识与示范。在MedDG与KaMed基准上的大量实验表明，MedRef在生成质量与医学实体准确性方面均优于当前最优基线，证明其在真实医疗应用中的有效性与可靠性。
原文摘要 · Abstract (English)
Medical dialogue systems (MDS) have emerged as crucial online platforms for enabling multi-turn, context-aware conversations with patients. However, existing MDS often struggle to (1) identify relevant medical knowledge and (2) generate personalized, medically accurate responses. To address these challenges, we propose MedRef, a novel MDS that incorporates knowledge refining and dynamic prompt adjustment. First, we employ a knowledge refining mechanism to filter out irrelevant medical data, improving predictions of critical medical entities in responses. Additionally, we design a comprehensive prompt structure that incorporates historical details and evident details. To enable real-time adaptability to diverse patient conditions, we implement two key modules, Triplet Filter and Demo Selector, providing appropriate knowledge and demonstrations equipped in the system prompt. Extensive experiments on MedDG and KaMed benchmarks show that MedRef outperforms state-of-the-art baselines in both generation quality and medical entity accuracy, underscoring its effectiveness and reliability for real-world healthcare applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。