大语言模型-教育方向数据集

编号	论文	数据集
1	Bitew S K, Hadifar A, Sterckx L, et al. Learning to Reuse Distractors to Support Multiple-Choice Question Generation in Education[J]. IEEE Transactions on Learning Technologies, 2022, 17: 375-390.	Televic, NL, https://github.com/semerekiros/dist-retrieval/tree/main/test-MCQs
2	QASC 问答数据集13小学科学选择题，每个问题包含8个选项，一个正确答案数据集介绍 QASC 是一个问答数据集。它包含 9,980 道关于小学科学的 8 项选择题（8,134 道题，926 道题，920 道题），并带有 1700 万个句子的语料库，数据集文件格式为jsonl。	https://aistudio.baidu.com/datasetdetail/105820
3	Cobbe K, Kosaraju V, Bavarian M, et al. Training verifiers to solve math word problems[J]. arXiv preprint arXiv:2110.14168, 2021.	GSM8K, EN, https://github.com/openai/grade-school-math
4	Hendrycks D, Burns C, Kadavath S, et al. Measuring mathematical problem solving with the math dataset[J]. arXiv preprint arXiv:2103.03874, 2021.	https://github.com/Khan/khan-exercises/, https://github.com/hendrycks/apps
5	Huang D, Shi S, Lin C Y, et al. How well do computers solve math word problems? large-scale dataset construction and evaluation[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016: 887-896.	Dolphin18K, https://www.microsoft.com/en-us/research/uploads/prod/2015/08/dolphin18k-v1.1.zip
6	Amini A, Gabriel S, Lin P, et al. Mathqa: Towards interpretable math word problem solving with operation-based formalisms[J]. arXiv preprint arXiv:1905.13319, 2019.	Mathqa https://math-qa.github.io/math-QA/
7	Miao S Y, Liang C C, Su K Y. A diverse corpus for evaluating and developing English math word problem solvers[J]. arXiv preprint arXiv:2106.15772, 2021.	ASDiv https://github.com/chaochun/nlu-asdiv-dataset/tree/master
8	Lu P, Qiu L, Chen J, et al. Iconqa: A new benchmark for abstract diagram understanding and visual language reasoning[J]. arXiv preprint arXiv:2110.13214, 2021.	IconQA, Visual Language Reasoning, https://iconqa.github.io/
9	Lu P, Gong R, Jiang S, et al. Inter-GPS: Interpretable geometry problem solving with formal language and symbolic reasoning[J]. arXiv preprint arXiv:2105.04165, 2021.	Geometry3K vision https://lupantech.github.io/inter-gps/
10	Pal A, Umapathi L K, Sankarasubbu M. Medmcqa: A large-scale multi-subject multi-choice dataset for medical domain question answering[C]//Conference on health, inference, and learning. PMLR, 2022: 248-260.	MedMCQA https://github.com/MedMCQA/MedMCQA