Analyzing and Mitigating Cross-Lingual Degradation in Multilingual Medical VQA
Medical visual question answering (VQA) is a crucial task in clinical AI, yet its evaluation has so far centered almost exclusively on English, limiting its relevance to linguistically diverse patients and clinicians. Recent multilingual medical VQA benchmarks show that large vision-language models (LVLMs) degrade in non-English languages, but lack a fine-grained analysis of how cross-lingual variation affects the distinct capabilities that medical VQA requires. To this end, we construct a multilingual medical VQA benchmark over eight languages, organized into four representative scenarios that isolate the core capabilities medical VQA requires. Evaluating five open- and closed-source LVL...
| Authors | Jingbo Wang, Sendong Zhao, Haochun Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 23 August 2026 |
| Last updated at source | 23 August 2026 |
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