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--- |
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dataset_info: |
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features: |
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- name: query |
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dtype: string |
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- name: choices |
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sequence: string |
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- name: gold |
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sequence: int64 |
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splits: |
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- name: test |
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num_bytes: 110388 |
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num_examples: 220 |
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download_size: 57020 |
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dataset_size: 110388 |
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configs: |
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- config_name: default |
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data_files: |
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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-sat-math" |
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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 SAT-Math 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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@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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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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@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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``` |
