首个孟加拉语医学问答数据集，提升低资源语言医疗AI准确性
BanglaMedQA and BanglaMMedBench: Evaluating Retrieval-Augmented Generation Strategies for Bangla Biomedical Question Answering
- 用OCR构建孟加拉语医学教材库，结合检索与生成策略
- 自适应代理RAG在测试集上达89.54%准确率，优于其他方法
- 适合多语言医疗AI、低资源语言研究者参考
在低资源语言中构建精准的生物医学问答系统仍面临重大挑战，限制了公平获取可靠医学知识。本文提出首个大规模孟加拉语生物医学多选题数据集BanglaMedQA和评估基准BanglaMMedBench，用于评测医学人工智能中的推理与检索能力。研究对比了多种检索增强生成（RAG）策略：传统、零样本回退、代理式、迭代反馈与聚合式RAG，结合教科书与网络检索，通过生成式推理提升事实准确性。关键创新在于通过光学字符识别（OCR）整合孟加拉语医学教材语料库，并构建动态选择检索或推理策略的代理式RAG流水线。实验表明，使用openai/gpt-oss-120b时，代理式RAG达到最高准确率89.54%，且推理理由质量更优。结果表明，基于RAG的方法可显著提升孟加拉语医学问答的可靠性与可及性，为多语言医学AI研究奠定基础。
原文摘要 · Abstract (English)
Developing accurate biomedical Question Answering (QA) systems in low-resource languages remains a major challenge, limiting equitable access to reliable medical knowledge. This paper introduces BanglaMedQA and BanglaMMedBench, the first large-scale Bangla biomedical Multiple Choice Question (MCQ) datasets designed to evaluate reasoning and retrieval in medical artificial intelligence (AI). The study applies and benchmarks several Retrieval-Augmented Generation (RAG) strategies, including Traditional, Zero-Shot Fallback, Agentic, Iterative Feedback, and Aggregate RAG, combining textbook-based and web retrieval with generative reasoning to improve factual accuracy. A key novelty lies in integrating a Bangla medical textbook corpus through Optical Character Recognition (OCR) and implementing an Agentic RAG pipeline that dynamically selects between retrieval and reasoning strategies. Experimental results show that the Agentic RAG achieved the highest accuracy 89.54% with openai/gpt-oss-120b, outperforming other configurations and demonstrating superior rationale quality. These findings highlight the potential of RAG-based methods to enhance the reliability and accessibility of Bangla medical QA, establishing a foundation for future research in multilingual medical artificial intelligence.
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