@inproceedings{shen-etal-2025-law,
    title = "A Law Reasoning Benchmark for {LLM} with Tree-Organized Structures including Factum Probandum, Evidence and Experiences",
    author = "Shen, Jiaxin  and
      Xu, Jinan  and
      Hu, Huiqi  and
      Lin, Luyi  and
      Ma, Guoyang  and
      Zheng, Fei  and
      Meng, Fandong  and
      Zhou, Jie  and
      Han, Wenjuan",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/mtsummit-25-ingestion/2025.findings-acl.887/",
    doi = "10.18653/v1/2025.findings-acl.887",
    pages = "17252--17274",
    ISBN = "979-8-89176-256-5",
    abstract = "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''."
}