用检索增强减少大模型医疗问答幻觉，提升诊断可靠性。
HALO: Hallucination Analysis and Learning Optimization to Empower LLMs with Retrieval-Augmented Context for Guided Clinical Decision Making
- 用大模型生成多版本问题，从外部知识库检索并加权融合上下文。
- 在Llama-3.1上准确率从44%提至65%，ChatGPT从56%提至70%。
- 适合医疗AI、临床决策系统开发者，可降低误诊风险。
大语言模型（LLMs）在自然语言处理任务中取得显著进展，但在健康与医学等关键领域，其生成的不准确或不可靠回答（即幻觉）可能带来严重风险。本文提出HALO框架，通过检测和缓解幻觉来提升医疗问答系统的准确性和可靠性。该方法利用大模型生成多个查询变体，并从外部开放知识库中检索相关信息以丰富上下文。采用最大边际相关性评分对检索内容进行优先排序，再将优化后的上下文提供给大模型生成答案，从而降低幻觉风险。通过集成LangChain进一步简化流程，使开源和商业大模型（如Llama-3.1和ChatGPT）的准确率均实现显著且稳健提升：Llama-3.1从44%升至65%，ChatGPT从56%升至70%。该框架凸显了在医疗问答中解决幻觉问题的重要性，有助于改善临床决策与患者护理。代码已开源：https://github.com/ResponsibleAILab/HALO。
原文摘要 · Abstract (English)
Large language models (LLMs) have significantly advanced natural language processing tasks, yet they are susceptible to generating inaccurate or unreliable responses, a phenomenon known as hallucination. In critical domains such as health and medicine, these hallucinations can pose serious risks. This paper introduces HALO, a novel framework designed to enhance the accuracy and reliability of medical question-answering (QA) systems by focusing on the detection and mitigation of hallucinations. Our approach generates multiple variations of a given query using LLMs and retrieves relevant information from external open knowledge bases to enrich the context. We utilize maximum marginal relevance scoring to prioritize the retrieved context, which is then provided to LLMs for answer generation, thereby reducing the risk of hallucinations. The integration of LangChain further streamlines this process, resulting in a notable and robust increase in the accuracy of both open-source and commercial LLMs, such as Llama-3.1 (from 44% to 65%) and ChatGPT (from 56% to 70%). This framework underscores the critical importance of addressing hallucinations in medical QA systems, ultimately improving clinical decision-making and patient care. The open-source HALO is available at: https://github.com/ResponsibleAILab/HALO.
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