Datasets:

hails
/

agieval-lsat-ar

	---
	dataset_info:
	features:
	- name: query
	dtype: string
	- name: choices
	sequence: string
	- name: gold
	sequence: int64
	splits:
	- name: test
	num_bytes: 273902
	num_examples: 230
	download_size: 66513
	dataset_size: 273902
	configs:
	- config_name: default
	data_files:
	- split: test
	path: data/test-*
	---
	# Dataset Card for "agieval-lsat-ar"


	Dataset taken from https://github.com/microsoft/AGIEval and processed as in that repo, following dmayhem93/agieval-* datasets on the HF hub.

	This dataset contains the contents of the LSAT analytical reasoning subtask of AGIEval, as accessed in https://github.com/ruixiangcui/AGIEval/commit/5c77d073fda993f1652eaae3cf5d04cc5fd21d40 .


	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}
	}
	```