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 |
https://arxiv.org/pdf/2403.18105v2
