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
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”.
BibTeX
@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"
}