ClinHallu: 用于诊断医学多模态大语言模型推理中阶段式幻觉的基准
ClinHallu: A Benchmark for Diagnosing Stage-Wise Hallucinations in Medical MLLM Reasoning
AI总结：
提出ClinHallu基准，包含7031个实例，每个实例带有结构化推理轨迹（视觉识别、知识回忆、推理整合），通过阶段替换干预和轨迹监督微调，实现细粒度幻觉诊断与缓解。
摘要：
构建可信的医学多模态大语言模型（MLLM）对于可靠的临床决策支持至关重要。现有的医学幻觉基准主要关注数据收集，但往往忽略了推理过程中幻觉的起源。我们发现幻觉来源因样本而异：错误可能源于视觉误识别、不正确的医学知识回忆或有缺陷的推理整合。为了实现源级别的幻觉诊断，我们引入了ClinHallu，一个用于医学MLLM推理中阶段式幻觉诊断的基准。ClinHallu包含7031个经过验证的实例，每个实例都附有分解为视觉识别、知识回忆和推理整合的结构化推理轨迹。我们还使用阶段替换干预来测量纠正特定阶段如何影响最终答案。除了评估，我们表明轨迹监督微调减少了阶段式幻觉。ClinHallu为诊断和缓解医学MLLM中的推理失败提供了一个细粒度的幻觉测试平台。该基准可从此https URL公开获取。
英文摘要：
Building trustworthy medical multimodal large language models (MLLMs) is critical for reliable clinical decision support. Existing medical hallucination benchmarks mainly focus on data collection, but often ignore where hallucinations originate within the reasoning process. We find that hallucination sources vary across samples: errors may arise from visual misrecognition, incorrect medical knowledge recall, or flawed reasoning integration. To enable source-level hallucination diagnosis, we introduce ClinHallu, a benchmark for stage-wise hallucination diagnosis in medical MLLM reasoning. ClinHallu contains 7,031 validated instances, where each instance is augmented with a structured reasoning trace decomposed into Visual Recognition, Knowledge Recall, and Reasoning Integration. We also use stage-replacement interventions to measure how correcting specific stages affects the final answer. Beyond evaluation, we show that trace-supervised fine-tuning reduces stage-wise hallucinations. ClinHallu provides a fine-grained hallucination testbed for diagnosing and mitigating reasoning failures in medical MLLMs. The benchmark is publicly available at https://github.com/alibaba-damo-academy/ClinHallu.
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。
