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RAGCare-QA: A Benchmark Dataset for Evaluating Retrieval-Augmented Generation Pipelines in Theoretical Medical Knowledge
2025-08-16
health informatics
Title + abstract only
View on medRxiv
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The paper introduces RAGCare-QA, an extensive dataset of 420 theoretical medical knowledge questions for assessing Retrieval-Augmented Generation (RAG) pipelines in medical education and evaluation settings. The dataset includes one-choice-only questions from six medical specialties (Cardiology, Endocrinology, Gastroenterology, Family Medicine, Oncology, and Neurology) with three levels of complexity (Basic, Intermediate, and Advanced). Each question is accompanied by the best fit of RAG impleme...
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