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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