How Good LLMs Are at Answering Bangla Medical Visual Questions? Dataset and Benchmarking
Recent advancements in Large Language Models (LLMs) and Large Vision Language Models (LVLMs) have enabled general-purpose systems to demonstrate promising capabilities in complex reasoning tasks, including those in the medical domain. Medical Visual Question Answering (MedVQA) has particularly benefited from these developments. However, despite Bangla being one of the most widely spoken languages globally, there exists no established MedVQA benchmark for it. To address this gap, we introduce BanglaMedVQA, a dataset comprising clinically validated image-question-answer pairs, along with a comprehensive evaluation of current foundation models on this resource. Consistent with prior findings...
| Authors | Rafid Ahmed, Intesar Tahmid, Mir Sazzat Hossain et al. |
|---|---|
| Primary category | cs.CL |
| Published | 18 May 2026 |
| Last updated at source | 18 May 2026 |
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