@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://aclanthology.org/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''."
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<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”.</abstract>
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%0 Conference Proceedings
%T A Law Reasoning Benchmark for LLM with Tree-Organized Structures including Factum Probandum, Evidence and Experiences
%A Shen, Jiaxin
%A Xu, Jinan
%A Hu, Huiqi
%A Lin, Luyi
%A Ma, Guoyang
%A Zheng, Fei
%A Meng, Fandong
%A Zhou, Jie
%A Han, Wenjuan
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F shen-etal-2025-law
%X 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”.
%R 10.18653/v1/2025.findings-acl.887
%U https://aclanthology.org/2025.findings-acl.887/
%U https://doi.org/10.18653/v1/2025.findings-acl.887
%P 17252-17274
Markdown (Informal)
[A Law Reasoning Benchmark for LLM with Tree-Organized Structures including Factum Probandum, Evidence and Experiences](https://aclanthology.org/2025.findings-acl.887/) (Shen et al., Findings 2025)
ACL
- Jiaxin Shen, Jinan Xu, Huiqi Hu, Luyi Lin, Guoyang Ma, Fei Zheng, Fandong Meng, Jie Zhou, and Wenjuan Han. 2025. A Law Reasoning Benchmark for LLM with Tree-Organized Structures including Factum Probandum, Evidence and Experiences. In Findings of the Association for Computational Linguistics: ACL 2025, pages 17252–17274, Vienna, Austria. Association for Computational Linguistics.