LogicPro: Improving Complex Logical Reasoning via Program-Guided Learning
Wangxuan Institute of Computer Technology, Peking University（北京大学信息科学技术研究院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Meituan Group（美团集团）；Institute of Trustworthy Embodied AI, Fudan University（复旦大学可信具身人工智能研究院）
Jin Jiang, Yuchen Yan, Yang Liu, Jianing Wang, Shuai Peng, Xunliang Cai, Yixin Cao, Mengdi Zhang, Liangcai Gao
备注：
19 pages, ACL 2025 (Volume 1 Long Papers), pages 26200-26218
In this paper, we propose a new data synthesis method called \textbf{LogicPro}, which leverages LeetCode-style algorithm \underline{Pro}blems and their corresponding \underline{Pro}gram solutions to synthesize Complex \underline{Logic}al 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 synthesize data that is difficult, scalable, effective, and comes with golden standard answers and high-quality reasoning processes. As a result, with our 540K synthesized dataset constructed solely from 2,360 algorithm problems, our approach \footnote{Code and data are publicly available at https://github.com/jiangjin1999/LogicPro} achieves significant improvements in multiple models for the datasets \textit{BBH$^{27}$}, \textit{LogicBench}, \textit{DROP}, \textit{AR-LSAT}, and \textit{GSM8K}, etc. outperforming a wide range of existing reasoning datasets.
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Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University（嵌入式系统与服务计算重点实验室，教育部，同济大学）；School of Computer Science and Technology, Tongji University（计算机科学与技术学院，同济大学）
Thinking with Drafting: Optical Decompression via Logical Reconstruction
Shenyang Institute of Computing Technology, Chinese Academy of Sciences（中国科学院沈阳计算技术研究所）；University of Chinese Academy of Sciences 3 ByteDance 4 Westlake University（中国科学院大学 3 字节跳动 4 西湖大学）；Key Laboratory of Computing Power Network（计算功率网络重点实验室）；Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences)（信息安全，教育部，山东计算机科学中心（济南国家超级计算中心），齐鲁工业大学（山东省科学院））
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Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Peng Cheng Laboratory（鹏城实验室）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation
Peking University（北京大学）；Center for LLM, Institute for Advanced Algorithms Research（大模型中心，高级算法研究所）；Huazhong University of Science and Technology（华中科技大学）
LLM-Guided Semantic Relational Reasoning for Multimodal Intent Recognition
Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；School of Information Science and Engineering, Hebei University of Science and Technology（河北科技大学信息科学与工程学院）
Com$^2$: A Causal-Guided Benchmark for Exploring Complex Commonsense Reasoning in Large Language Models
Research Center for Social Computing and Interactive Robotics（社会计算与交互机器人研究中心）；Harbin Institute of Technology（哈尔滨工业大学）；Institute of Trustworthy Embodied AI（可信具身人工智能研究院）；Fudan University（复旦大学）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Nanjing University（南京大学）
ChainReaction: Causal Chain-Guided Reasoning for Modular and Explainable Causal-Why Video Question Answering
Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore（新加坡高性能计算研究所，科技研究局）；Centre for Frontier AI Research, Agency for Science, Technology and Research, Singapore（科技研究局前沿人工智能研究中心，新加坡）；College of Computing and Data Science, Nanyang Technological University, Singapore（新加坡南洋理工大学计算与数据科学学院）
