MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering
It provides a new benchmark for evaluating AI models on real-world medical domain tasks, addressing a gap in high-quality, diverse medical QA data.
The paper introduces MedMCQA, a large-scale multiple-choice question answering dataset with over 194k medical exam questions covering 2.4k topics and 21 subjects, designed to test deep language understanding and reasoning abilities.
This paper introduces MedMCQA, a new large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions. More than 194k high-quality AIIMS \& NEET PG entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average token length of 12.77 and high topical diversity. Each sample contains a question, correct answer(s), and other options which requires a deeper language understanding as it tests the 10+ reasoning abilities of a model across a wide range of medical subjects \& topics. A detailed explanation of the solution, along with the above information, is provided in this study.