ClinHallu:用于诊断医疗MLLM推理中阶段性幻觉的基准测试
阅读原文· arxiv.orgClinHallu是一个面向医疗多模态大语言模型(MLLM)的阶段性幻觉诊断基准。该基准包含7031个经过验证的实例,每个实例都带有结构化的推理追踪,分解为视觉识别、知识召回和推理整合三个阶段。通过阶段替换干预,可测量修正特定阶段对最终答案的影响。实验表明,追踪监督微调能减少阶段性的模型幻觉。ClinHallu为诊断和缓解医疗MLLM中的推理故障提供了细粒度的测试平台,并已公开提供。
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.