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ACL Findings2025

Can We Trust AI Doctors? A Survey of Medical Hallucination in Large Language and Large Vision-Language Models

Zhihong Zhu, Yunyan Zhang, Xianwei Zhuang, Fan Zhang, Zhongwei Wan, Yuyan Chen, Qingqing Long, Yefeng Zheng, Xian Wu

摘要

Hallucination has emerged as a critical challenge for large language models (LLMs) and large vision-language models (LVLMs), particularly in high-stakes medical applications. Despite its significance, dedicated research on medical hallucination remains unexplored. In this survey, we first provide a unified perspective on medical hallucination for both LLMs and LVLMs, and delve into its causes. Subsequently, we review recent advancements in detecting, evaluating, and mitigating medical hallucinations, offering a comprehensive overview of evaluation benchmarks, metrics, and strategies developed to tackle this issue. Moreover, we delineate the current challenges and delve into new frontiers, thereby shedding light on future research. We hope this work can provide the community with quick access and spur breakthrough research in this area.

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引用它的顶会 Paper4

  1. MME-Emotion: A Holistic Evaluation Benchmark for Emotional Intelligence in Multimodal Large Language ModelsFan Zhang, Zebang Cheng, Chong Deng, Haoxuan Li 等ICLR 2026 · 23 次引用
  2. A Survey on Foundation Language Models for Single-cell BiologyFan Zhang, Hao Chen, Zhihong Zhu, Ziheng Zhang 等ACL 2025 · 10 次引用
  3. CMedCalc-Bench: A Fine-Grained Benchmark for Chinese Medical Calculations in LLMYunyan Zhang, Zhihong Zhu, Xian WuEMNLP 2025 · 1 次引用
  4. MEDA: Medical-Oriented Activation Editing for Hallucination Mitigation in Medical Large Vision-Language ModelTianbo Wang, Yuqing Ma, Lingyan Meng, Zhange Zhang 等ICML 2026

它引用的 Paper46

  1. Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah 等NeurIPS 2020 · 64,255 次引用
  2. Chain-of-Thought Prompting Elicits Reasoning in Large Language ModelsJason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma 等NeurIPS 2022 · 22,562 次引用
  3. Retrieval-Augmented Generation for Knowledge-Intensive NLP TasksPatrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni 等NeurIPS 2020 · 19,162 次引用
  4. Visual Instruction TuningHaotian Liu, Chunyuan Li, Qingyang Wu, Yong Jae LeeNeurIPS 2023 · 11,349 次引用
  5. Supervised Contrastive LearningPrannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna 等NeurIPS 2020 · 7,049 次引用

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