ArXiv

ClinHallu: A Benchmark for Diagnosing Stage-Wise Hallucinations in Medical MLLM Reasoning

2026-06-12 · 17:58 UTC ·Sicheng Yang, Hangjie Yuan, Wenjun Zhang... ·1 min read
Authors
Sicheng Yang, Hangjie Yuan, Wenjun Zhang...
Categories
cs.CV, cs.AI, cs.CL
arXiv
https://arxiv.org/abs/2606.14697v1
PDF
https://arxiv.org/pdf/2606.14697v1

Brief

ClinHallu targets stage-wise hallucinations in medical multimodal LLMs by annotating 7,031 instances with decomposed reasoning traces (Visual Recognition, Knowledge Recall, Reasoning Integration). The benchmark uses stage-replacement interventions to measure error sources and demonstrates that trace-supervised fine-tuning mitigates stage-specific hallucinations, providing a fine-grained testbed and mitigation pathway for clinical MLLM failures.

Source evidence

Abstract

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.

Comment: Code and datasets: https://github.com/alibaba-damo-academy/ClinHallu