医学问答中用因果链式推理提升准确率
MedCoT-RAG: Causal Chain-of-Thought RAG for Medical Question Answering
- 基于因果逻辑检索医学文档并生成分步推理
- 在3个基准上比基线最高提升10.3%
- 适合需要可解释临床决策的医疗AI场景
大型语言模型在医学问答中展现潜力，但在需要细致临床理解的任务中常出现幻觉和浅层推理。检索增强生成（RAG）为增强模型外部医学知识提供了实用且保护隐私的方案。然而，现有方法多依赖表层语义检索，缺乏临床决策支持所需的结构化推理。我们提出MedCoT-RAG，一种结合因果感知文档检索与面向医疗流程的结构化链式思考提示的领域专用框架。该设计使模型能检索符合诊断逻辑的证据，并生成反映真实临床实践的逐步因果推理。在三个多样化的医学问答基准上的实验表明，MedCoT-RAG相比基线最高提升10.3%（相对于原始RAG），比先进领域适配方法提升6.4%，显著提高了复杂医疗任务中的准确性、可解释性和一致性。
原文摘要 · Abstract (English)
Large language models (LLMs) have shown promise in medical question answering but often struggle with hallucinations and shallow reasoning, particularly in tasks requiring nuanced clinical understanding. Retrieval-augmented generation (RAG) offers a practical and privacy-preserving way to enhance LLMs with external medical knowledge. However, most existing approaches rely on surface-level semantic retrieval and lack the structured reasoning needed for clinical decision support. We introduce MedCoT-RAG, a domain-specific framework that combines causal-aware document retrieval with structured chain-of-thought prompting tailored to medical workflows. This design enables models to retrieve evidence aligned with diagnostic logic and generate step-by-step causal reasoning reflective of real-world clinical practice. Experiments on three diverse medical QA benchmarks show that MedCoT-RAG outperforms strong baselines by up to 10.3% over vanilla RAG and 6.4% over advanced domain-adapted methods, improving accuracy, interpretability, and consistency in complex medical tasks.
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。