MedHallTune：用于缓解视觉-语言模型中医患幻觉的指令调优基准
MedHallTune: An Instruction-Tuning Benchmark for Mitigating Medical Hallucination in Vision-Language Models
AI总结：
本研究提出MedHallTune基准，用于评估和缓解医疗视觉-语言模型的幻觉，实验表明用该基准微调模型可提升幻觉管理能力与下游VQA任务零样本性能，助力开发更可信的医疗视觉-语言模型。
摘要：
视觉-语言模型（VLMs）在医疗应用中的日益使用带来了与幻觉相关的重大挑战，模型可能生成看似合理实则错误的结果，此类幻觉会危及临床决策，可能损害诊断与治疗。本研究提出MedHallTune，一个专为评估和缓解医疗VLMs中幻觉设计的大规模基准，包含超10万张图像与100万条指令对，涵盖幻觉及非幻觉样本，均带有真实标注。我们使用MedHallTune对当前医疗及通用VLMs开展全面评估，评估其在临床准确率、相关性、细节程度、风险等级等关键指标上的表现。实验结果显示，使用MedHallTune进行微调可成功提升现有多款模型的幻觉管理能力，并增强其在下游视觉问答（VQA）任务上的零样本性能，使其更适用于实际医疗应用，本研究为开发更可信的VLMs作出贡献，代码与数据集将在MedHallTune网址公开。
英文摘要：
The increasing use of vision-language models (VLMs) in healthcare applications presents great challenges related to hallucinations, in which the models may generate seemingly plausible results that are in fact incorrect. Such hallucinations can jeopardize clinical decision making, potentially harming the diagnosis and treatments. In this work, we propose MedHallTune, a large-scale benchmark designed specifically to evaluate and mitigate hallucinations in medical VLMs. Comprising over 100,000 images and 1,000,000 instruction pairs, MedHallTune includes both hallucination and non-hallucination samples, each with ground-truth annotations. We conduct a comprehensive evaluation of current medical and general VLMs using MedHallTune, assessing their performance across key metrics, including clinical accuracy, relevance, detail level, and risk level. The experimental results show that fine-tuning with MedHallTune successfully improves the ability of several existing models to manage hallucinations and boost their zero-shot performance on downstream visual-question-answering (VQA) tasks, making them more reliable for practical medical applications. Our work contributes to the development of more trustworthy VLMs. Codes and dataset will be available at \href{https://github.com/russellyq/MedHallTune}{MedHallTune}.
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