| # AGIEval | |
| ### Paper | |
| Title: AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models | |
| Abstract: https://arxiv.org/abs/2304.06364.pdf | |
| 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. | |
| Homepage: https://github.com/ruixiangcui/AGIEval | |
| ### Citation | |
| ``` | |
| @misc{zhong2023agieval, | |
| title={AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models}, | |
| 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}, | |
| year={2023}, | |
| eprint={2304.06364}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CL} | |
| } | |
| ``` | |
| Please make sure to cite all the individual datasets in your paper when you use them. We provide the relevant citation information below: | |
| ``` | |
| @inproceedings{ling-etal-2017-program, | |
| title = "Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems", | |
| author = "Ling, Wang and | |
| Yogatama, Dani and | |
| Dyer, Chris and | |
| Blunsom, Phil", | |
| booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)", | |
| month = jul, | |
| year = "2017", | |
| address = "Vancouver, Canada", | |
| publisher = "Association for Computational Linguistics", | |
| url = "https://aclanthology.org/P17-1015", | |
| doi = "10.18653/v1/P17-1015", | |
| pages = "158--167", | |
| 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.", | |
| } | |
| @inproceedings{hendrycksmath2021, | |
| title={Measuring Mathematical Problem Solving With the MATH Dataset}, | |
| author={Dan Hendrycks and Collin Burns and Saurav Kadavath and Akul Arora and Steven Basart and Eric Tang and Dawn Song and Jacob Steinhardt}, | |
| journal={NeurIPS}, | |
| year={2021} | |
| } | |
| @inproceedings{Liu2020LogiQAAC, | |
| title={LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning}, | |
| author={Jian Liu and Leyang Cui and Hanmeng Liu and Dandan Huang and Yile Wang and Yue Zhang}, | |
| booktitle={International Joint Conference on Artificial Intelligence}, | |
| year={2020} | |
| } | |
| @inproceedings{zhong2019jec, | |
| title={JEC-QA: A Legal-Domain Question Answering Dataset}, | |
| author={Zhong, Haoxi and Xiao, Chaojun and Tu, Cunchao and Zhang, Tianyang and Liu, Zhiyuan and Sun, Maosong}, | |
| booktitle={Proceedings of AAAI}, | |
| year={2020}, | |
| } | |
| @article{Wang2021FromLT, | |
| title={From LSAT: The Progress and Challenges of Complex Reasoning}, | |
| author={Siyuan Wang and Zhongkun Liu and Wanjun Zhong and Ming Zhou and Zhongyu Wei and Zhumin Chen and Nan Duan}, | |
| journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing}, | |
| year={2021}, | |
| volume={30}, | |
| pages={2201-2216} | |
| } | |
| ``` | |
| ### Groups and Tasks | |
| #### Groups | |
| - `agieval`: Evaluates all tasks listed below. | |
| - `agieval_en`: Evaluates all English subtasks: `agieval_aqua_rat`, `agieval_gaokao_english`, `agieval_logiqa_en`, `agieval_lsat_*`, `agieval_sat_*`, `agieval_math` | |
| - `agieval_cn`: Evaluates all Chinese subtasks: | |
| `agieval_gaokao_biology`, `agieval_gaokao_chemistry`, `agieval_gaokao_chinese`, `agieval_gaokao_geography`, | |
| `agieval_gaokao_history`, `agieval_gaokao_mathqa`, `agieval_gaokao_mathcloze`, `agieval_gaokao_physics`, `agieval_jec_qa_ca`, `agieval_jec_qa_kd`, `agieval_logiqa_zh` | |
| - `agieval_nous`: Evaluates a specific subset of AGIEval tasks (multiple-choice and english-only), namely those in https://github.com/teknium1/LLM-Benchmark-Logs/blob/main/benchmark-logs/Mistral-7B-Base.md | |
| #### Tasks | |
| - `agieval_aqua_rat` | |
| - `agieval_gaokao_biology` | |
| - `agieval_gaokao_chemistry` | |
| - `agieval_gaokao_chinese` | |
| - `agieval_gaokao_english` | |
| - `agieval_gaokao_geography` | |
| - `agieval_gaokao_history` | |
| - `agieval_gaokao_mathqa` | |
| - `agieval_gaokao_mathcloze` | |
| - `agieval_gaokao_physics` | |
| - `agieval_jec_qa_ca` | |
| - `agieval_jec_qa_kd` | |
| - `agieval_logiqa_en` | |
| - `agieval_logiqa_zh` | |
| - `agieval_lsat_ar` | |
| - `agieval_lsat_lr` | |
| - `agieval_lsat_rc` | |
| - `agieval_sat_en` | |
| - `agieval_sat_en_without_passage` | |
| - `agieval_sat_math` | |
| - `agieval_math` | |