Hallucination Benchmark in Medical Visual Question Answering

• URL: http://arxiv.org/abs/2401.05827v2
• Date: Wed, 3 Apr 2024 12:42:32 GMT
• Title: Hallucination Benchmark in Medical Visual Question Answering
• Authors: Jinge Wu, Yunsoo Kim, Honghan Wu,
• Abstract summary: 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.
• Score: 2.4302611783073145
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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