{
 "id": "HPAI-BSC/medmcqa-cot-llama31",
 "owner": "HPAI-BSC",
 "name": "medmcqa-cot-llama31",
 "downloads": 2587,
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 "last_modified": "2025-04-02T15:17:29.000Z",
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 "tasks": [
  "multiple-choice",
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 ],
 "citation": "@misc{gururajan2024aloe,\n      title={Aloe: A Family of Fine-tuned Open Healthcare LLMs}, \n      author={Ashwin Kumar Gururajan and Enrique Lopez-Cuena and Jordi Bayarri-Planas and Adrian Tormos and Daniel Hinjos and Pablo Bernabeu-Perez and Anna Arias-Duart and Pablo Agustin Martin-Torres and Lucia Urcelay-Ganzabal and Marta Gonzalez-Mallo and Sergio Alvarez-Napagao and Eduard Ayguadé-Parra and Ulises Cortés Dario Garcia-Gasulla},\n      year={2024},\n      eprint={2405.01886},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n\n@InProceedings{pmlr-v174-pal22a,\n  title = \t {MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering},\n  author =       {Pal, Ankit and Umapathi, Logesh Kumar and Sankarasubbu, Malaikannan},\n  booktitle = \t {Proceedings of the Conference on Health, Inference, and Learning},\n  pages = \t {248--260},\n  year = \t {2022},\n  editor = \t {Flores, Gerardo and Chen, George H and Pollard, Tom and Ho, Joyce C and Naumann, Tristan},\n  volume = \t {174},\n  series = \t {Proceedings of Machine Learning Research},\n  month = \t {07--08 Apr},\n  publisher =    {PMLR},\n  pdf = \t {https://proceedings.mlr.press/v174/pal22a/pal22a.pdf},\n  url = \t {https://proceedings.mlr.press/v174/pal22a.html},\n  abstract = \t {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 MCQ",
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