Guoyang Ma


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2025

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A Law Reasoning Benchmark for LLM with Tree-Organized Structures including Factum Probandum, Evidence and Experiences
Jiaxin Shen | Jinan Xu | Huiqi Hu | Luyi Lin | Guoyang Ma | Fei Zheng | Fandong Meng | Jie Zhou | Wenjuan Han
Findings of the Association for Computational Linguistics: ACL 2025

While progress has been made in legal applications, law reasoning, crucial for fair adjudication, remains unexplored. We propose a transparent law reasoning schema enriched with hierarchical factum probandum, evidence, and implicit experience, enabling public scrutiny and preventing bias. Inspired by this schema, we introduce the challenging task, which takes a textual case description and outputs a hierarchical structure justifying the final decision. We also create the first crowd-sourced dataset for this task, enabling comprehensive evaluation. Simultaneously, we propose TL agent that employs a comprehensive suite of legal analysis tools to address the challenge task. This benchmark paves the way for transparent and accountable AI-assisted law-reasoning in the “Intelligent Court”.