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--- |
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license: mit |
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language: |
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- en |
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task_categories: |
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- question-answering |
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- text-generation |
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pretty_name: AGIEval |
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--- |
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# Dataset Card for AGIEval |
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## Dataset Description |
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- **Homepage:** https://github.com/microsoft/AGIEval/blob/main/README.md |
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- **Repository:** https://github.com/microsoft/AGIEval |
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- **Paper:** https://arxiv.org/abs/2304.06364 |
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### Dataset Summary |
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AGIEval is a human-centric benchmark specifically designed to evaluate the general abilities of foundation models in tasks pertinent to human cognition and problem-solving. This benchmark is derived from 20 official, public, and high-standard admission and qualification exams intended for general human test-takers, such as general college admission tests (e.g., Chinese College Entrance Exam (Gaokao) and American SAT), law school admission tests, math competitions, lawyer qualification tests, and national civil service exams. |
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### Citation Information |
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Dataset taken from the AGIEval Repo. |
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``` |
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@misc{zhong2023agieval, |
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title={AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models}, |
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author={Wanjun Zhong and Ruixiang Cui and Yiduo Guo and Yaobo Liang and Shuai Lu and Yanlin Wang and Amin Saied and Weizhu Chen and Nan Duan}, |
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year={2023}, |
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eprint={2304.06364}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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### Citation for each dataset: |
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``` |
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@inproceedings{ling-etal-2017-program, |
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title = "Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems", |
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author = "Ling, Wang and |
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Yogatama, Dani and |
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Dyer, Chris and |
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Blunsom, Phil", |
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booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", |
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month = jul, |
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year = "2017", |
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address = "Vancouver, Canada", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/P17-1015", |
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doi = "10.18653/v1/P17-1015", |
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pages = "158--167", |
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abstract = "Solving algebraic word problems requires executing a series of arithmetic operations{---}a program{---}to obtain a final answer. However, since programs can be arbitrarily complicated, inducing them directly from question-answer pairs is a formidable challenge. To make this task more feasible, we solve these problems by generating answer rationales, sequences of natural language and human-readable mathematical expressions that derive the final answer through a series of small steps. Although rationales do not explicitly specify programs, they provide a scaffolding for their structure via intermediate milestones. To evaluate our approach, we have created a new 100,000-sample dataset of questions, answers and rationales. Experimental results show that indirect supervision of program learning via answer rationales is a promising strategy for inducing arithmetic programs.", |
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} |
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@inproceedings{hendrycksmath2021, |
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title={Measuring Mathematical Problem Solving With the MATH Dataset}, |
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author={Dan Hendrycks and Collin Burns and Saurav Kadavath and Akul Arora and Steven Basart and Eric Tang and Dawn Song and Jacob Steinhardt}, |
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journal={NeurIPS}, |
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year={2021} |
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} |
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@inproceedings{Liu2020LogiQAAC, |
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title={LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning}, |
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author={Jian Liu and Leyang Cui and Hanmeng Liu and Dandan Huang and Yile Wang and Yue Zhang}, |
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booktitle={International Joint Conference on Artificial Intelligence}, |
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year={2020} |
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} |
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@inproceedings{zhong2019jec, |
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title={JEC-QA: A Legal-Domain Question Answering Dataset}, |
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author={Zhong, Haoxi and Xiao, Chaojun and Tu, Cunchao and Zhang, Tianyang and Liu, Zhiyuan and Sun, Maosong}, |
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booktitle={Proceedings of AAAI}, |
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year={2020}, |
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} |
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@article{Wang2021FromLT, |
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title={From LSAT: The Progress and Challenges of Complex Reasoning}, |
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author={Siyuan Wang and Zhongkun Liu and Wanjun Zhong and Ming Zhou and Zhongyu Wei and Zhumin Chen and Nan Duan}, |
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journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing}, |
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year={2021}, |
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volume={30}, |
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pages={2201-2216} |
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} |
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``` |
