Update README.md

README.md

@@ -20,4 +20,75 @@ configs:
     path: data/test-*
 language:
 - zh
----
+---
+
+# Dataset Card for "agieval-jec-qa-ca"
+
+
+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 JEC-QA-CA 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}
+}
+```