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  - split: test
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  path: data/test-*
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  - split: test
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  path: data/test-*
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  ---
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+ # Dataset Card for "agieval-lsat-ar"
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+ Dataset taken from https://github.com/microsoft/AGIEval and processed as in that repo, following dmayhem93/agieval-* datasets on the HF hub.
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+ This dataset contains the contents of the LSAT analytical reasoning subtask of AGIEval, as accessed in https://github.com/ruixiangcui/AGIEval/commit/5c77d073fda993f1652eaae3cf5d04cc5fd21d40 .
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+ Citation:
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+
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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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+
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+ Please make sure to cite all the individual datasets in your paper when you use them. We provide the relevant citation information below:
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+
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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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+
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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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+
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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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+
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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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+
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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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+ ```
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+