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Bridging the Semantic Gaps: Improving MVQA Consistency with LLM-Augmented Question Sets

2025-06-24

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<title>Abstract</title> <p><bold>Purpose:</bold> We confront a critical yet under-studied weakness of Medical Visual Question Answering (MVQA): models often flip their answers when clinicians phrase the same diagnostic query differently. We ask whether large-language model-driven data augmentation can deliver paraphrase-proof MVQA. <bold>Methods: </bold>Our Semantically Equivalent Question Augmentation (SEQA) pip...

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Literature Corpus work: 73a22f77-6aa0-566f-bdb7-6f4d059724b3
DOI: 10.21203/rs.3.rs-6867575/v1

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