医学视觉问答中的幻觉基准
Hallucination Benchmark in Medical Visual Question Answering
AI总结：
针对Med-VQA中LLVM幻觉缺乏临床评测的问题，本文构建医学图像问答幻觉基准，评估前沿模型并分析提示策略的效果。
摘要：
大型语言与视觉模型（LLVMs）近期在视觉问答（VQA）上的成功，尤其是其在医学（Med-VQA）中的应用，展现了实现高效医疗视觉助手的巨大潜力。然而，这些模型尚未在临床环境中针对幻觉现象接受广泛测试。为此，我们构建了一个由医学图像及配套问答集组成的幻觉基准，并对最先进模型进行了综合评估。本研究深入分析了当前模型的局限性，并揭示了各种提示策略的有效性。
英文摘要：
The recent success of large language and vision models (LLVMs) on vision question answering (VQA), particularly their applications in medicine (Med-VQA), has shown a great potential of realizing effective visual assistants for healthcare. However, these models are not extensively tested on the hallucination phenomenon in clinical settings. Here, we created a hallucination benchmark of medical images paired with question-answer sets and conducted a comprehensive evaluation of the state-of-the-art models. The study provides an in-depth analysis of current models' limitations and reveals the effectiveness of various prompting strategies.
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。
