{
 "id": "jiangjin/LogicPro",
 "owner": "jiangjin",
 "name": "LogicPro",
 "downloads": 171,
 "likes": 6,
 "last_modified": "2025-07-29T20:51:52.000Z",
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 "citation": "@inproceedings{jiang-etal-2025-logicpro,\n    title = \"{L}ogic{P}ro: Improving Complex Logical Reasoning via Program-Guided Learning\",\n    author = \"Jiang, Jin  and\n      Yan, Yuchen  and\n      Liu, Yang  and\n      Wang, Jianing  and\n      Peng, Shuai  and\n      Cai, Xunliang  and\n      Cao, Yixin  and\n      Zhang, Mengdi  and\n      Gao, Liangcai\",\n    editor = \"Che, Wanxiang  and\n      Nabende, Joyce  and\n      Shutova, Ekaterina  and\n      Pilehvar, Mohammad Taher\",\n    booktitle = \"Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",\n    month = jul,\n    year = \"2025\",\n    address = \"Vienna, Austria\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2025.acl-long.1270/\",\n    pages = \"26200--26218\",\n    ISBN = \"979-8-89176-251-0\",\n    abstract = \"In this paper, we propose a new data synthesis method called \\textbf{LogicPro}, which leverages LeetCode-style algorithm Problems and their corresponding Program solutions to synthesize Complex Logical Reasoning data in text format. First, we synthesize complex reasoning problems through source algorithm problems and test cases. Then, standard answers and intermediate variable outputs are obtained for each problem based on standard python solutions and test cases. Finally, with the guidance of code intermediate variables, we synthesize the text reasoning process for each reasoning problems. Through this method, we can synthes",
 "signal": "permissive",
 "reason": "The declared licence (apache-2.0) is classified as permissive.",
 "revision": "23556b176a25fa0d3423dabb55e9a7decd1c892c",
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}