数据集:

lighteval/med_mcqa

预印本库:

arxiv:2203.14371
中文

From "MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering" (Pal et al.), MedMCQA is a "multiple-choice question answering (MCQA) dataset designed to address real-world medical entrance exam questions." The dataset "...has 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."

The following is an example from the dataset:

Question: In a patient of heart disease antibiotic prophylaxis for dental extraction is: A. Amoxicillin. B. Imipenem. C. Gentamicin. D. Erythromycin. Answer: A

Paper: https://arxiv.org/abs/2203.14371 Code: https://github.com/MedMCQA/MedMCQA

@InProceedings{pmlr-v174-pal22a,
  title = 	  {MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering},
  author =    {Pal, Ankit and Umapathi, Logesh Kumar and Sankarasubbu, Malaikannan},
  booktitle = {Proceedings of the Conference on Health, Inference, and Learning},
  pages = 	 {248--260},
  year = 	 {2022},
  editor = 	 {Flores, Gerardo and Chen, George H and Pollard, Tom and Ho, Joyce C and Naumann, Tristan},
  volume = 	 {174},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {07--08 Apr},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v174/pal22a/pal22a.pdf},
  url = 	 {https://proceedings.mlr.press/v174/pal22a.html},
  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.}
}