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Computer Science > Computation and Language

arXiv:2203.14371 (cs)
[Submitted on 27 Mar 2022]

Title:MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering

Authors:Ankit Pal, Logesh Kumar Umapathi, Malaikannan Sankarasubbu
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Abstract: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.
Comments: Proceedings of Machine Learning Research (PMLR), ACM Conference on Health, Inference, and Learning (CHIL) 2022
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2203.14371 [cs.CL]
  (or arXiv:2203.14371v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2203.14371
Journal reference: ACM Conference on Health, Inference, and Learning (CHIL) 2022

Submission history

From: Ankit Pal [view email]
[v1] Sun, 27 Mar 2022 18:59:16 UTC (871 KB)
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