Update README.md

README.md

@@ -13,7 +13,50 @@ dataset_info:
     num_examples: 254
   download_size: 0
   dataset_size: 93696
+license: apache-2.0
 ---
 # Dataset Card for "agieval-aqua-rat"
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+Dataset taken from https://github.com/microsoft/AGIEval and processed as in that repo.
+
+Raw dataset: https://github.com/deepmind/AQuA
+
+Copyright 2017 Google Inc.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+@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}
+}
+
+@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.",
+}