MedHallBench：医学大型语言模型幻觉评估的新基准
MedHallBench: A New Benchmark for Assessing Hallucination in Medical Large Language Models
AI总结：
该研究提出MedHallBench基准，结合专家验证病例与ACHMI自动评分及RLHF训练，全面评估并缓解医学大语言模型幻觉，提升其医疗可靠性。
摘要：
医学大型语言模型（MLLMs）在医疗保健应用中展现出潜力，然而其产生幻觉的倾向——即生成医学上不合理或不准确的信息——对患者护理构成了重大风险。本文介绍了MedHallBench，一个用于评估和缓解MLLMs幻觉的全面基准框架。我们的方法将专家验证的医学病例场景与既有医学数据库相结合，构建了一个稳健的评估数据集。该框架采用了一套精密的测量系统，将自动ACHMI（医学影像自动字幕幻觉测量）评分与严格的临床专家评估相结合，并利用强化学习方法实现自动标注。通过一个专为医学应用设计的优化强化学习从人类反馈（RLHF）训练流程，MedHallBench能够在多样化的临床情境中对MLLMs进行全面评估，同时保持严格的准确性标准。我们进行了涉及多种模型的对比实验，利用该基准为广泛采用的大型语言模型（LLMs）建立了基线。我们的研究结果表明，与传统指标相比，ACHMI能够更细致地理解幻觉的影响，从而凸显了其在幻觉评估中的优势。这项研究为提升MLLMs在医疗保健环境中的可靠性奠定了基础框架，并为应对医学应用中AI幻觉这一关键挑战提供了切实可行的策略。
英文摘要：
Medical Large Language Models (MLLMs) have demonstrated potential in healthcare applications, yet their propensity for hallucinations -- generating medically implausible or inaccurate information -- presents substantial risks to patient care. This paper introduces MedHallBench, a comprehensive benchmark framework for evaluating and mitigating hallucinations in MLLMs. Our methodology integrates expert-validated medical case scenarios with established medical databases to create a robust evaluation dataset. The framework employs a sophisticated measurement system that combines automated ACHMI (Automatic Caption Hallucination Measurement in Medical Imaging) scoring with rigorous clinical expert evaluations and utilizes reinforcement learning methods to achieve automatic annotation. Through an optimized reinforcement learning from human feedback (RLHF) training pipeline specifically designed for medical applications, MedHallBench enables thorough evaluation of MLLMs across diverse clinical contexts while maintaining stringent accuracy standards. We conducted comparative experiments involving various models, utilizing the benchmark to establish a baseline for widely adopted large language models (LLMs). Our findings indicate that ACHMI provides a more nuanced understanding of the effects of hallucinations compared to traditional metrics, thereby highlighting its advantages in hallucination assessment. This research establishes a foundational framework for enhancing MLLMs' reliability in healthcare settings and presents actionable strategies for addressing the critical challenge of AI hallucinations in medical applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
