@inproceedings{xu-etal-2025-clear,
title = "{CLEAR}: A Framework Enabling Large Language Models to Discern Confusing Legal Paragraphs",
author = "Xu, Qi and
Liu, Qian and
Fei, Hao and
Yu, Hang and
Guan, Shuhao and
Wei, Xiao",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.475/",
pages = "8937--8953",
ISBN = "979-8-89176-335-7",
abstract = "Most of the existing work focuses on enabling LLMs to leverage legal rules (, law articles) to tackle complex legal reasoning tasks, but ignores their ability to understand legal rules. To better evaluate the LLMs' capabilities on the task, in this work, we propose a new challenge task: Legal Paragraph Prediction (LPP), which aims to predict the legal paragraph given criminal facts. Moreover, to enhance the legal reasoning ability of LLMs, we propose a novel framework CLEAR, enabling LLMs to analyze legal cases with the guidance of legal rule insights. The CLEAR contains four key components, where the \textit{Legal Rules Retriever} aims to retrieve legal rule knowledge, and the \textit{Rule Insights Generator} is used to generate legal insights guiding the LLM{'}s reasoning, then the \textit{Case Analyzer} analyze the case with the guidance of legal rule insights given criminal facts. Finally, the \textit{Legal Reasoner} synthesizes the criminal facts, legal rule insights, and analysis results to derive the final decision. By conducting extensive experiments on a real-world dataset, experimental results validate the effectiveness of our proposed model. Our codes and dataset are available at \url{https://anonymous.4open.science/r/CLEAR-3048}."
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<abstract>Most of the existing work focuses on enabling LLMs to leverage legal rules (, law articles) to tackle complex legal reasoning tasks, but ignores their ability to understand legal rules. To better evaluate the LLMs’ capabilities on the task, in this work, we propose a new challenge task: Legal Paragraph Prediction (LPP), which aims to predict the legal paragraph given criminal facts. Moreover, to enhance the legal reasoning ability of LLMs, we propose a novel framework CLEAR, enabling LLMs to analyze legal cases with the guidance of legal rule insights. The CLEAR contains four key components, where the Legal Rules Retriever aims to retrieve legal rule knowledge, and the Rule Insights Generator is used to generate legal insights guiding the LLM’s reasoning, then the Case Analyzer analyze the case with the guidance of legal rule insights given criminal facts. Finally, the Legal Reasoner synthesizes the criminal facts, legal rule insights, and analysis results to derive the final decision. By conducting extensive experiments on a real-world dataset, experimental results validate the effectiveness of our proposed model. Our codes and dataset are available at https://anonymous.4open.science/r/CLEAR-3048.</abstract>
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%0 Conference Proceedings
%T CLEAR: A Framework Enabling Large Language Models to Discern Confusing Legal Paragraphs
%A Xu, Qi
%A Liu, Qian
%A Fei, Hao
%A Yu, Hang
%A Guan, Shuhao
%A Wei, Xiao
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F xu-etal-2025-clear
%X Most of the existing work focuses on enabling LLMs to leverage legal rules (, law articles) to tackle complex legal reasoning tasks, but ignores their ability to understand legal rules. To better evaluate the LLMs’ capabilities on the task, in this work, we propose a new challenge task: Legal Paragraph Prediction (LPP), which aims to predict the legal paragraph given criminal facts. Moreover, to enhance the legal reasoning ability of LLMs, we propose a novel framework CLEAR, enabling LLMs to analyze legal cases with the guidance of legal rule insights. The CLEAR contains four key components, where the Legal Rules Retriever aims to retrieve legal rule knowledge, and the Rule Insights Generator is used to generate legal insights guiding the LLM’s reasoning, then the Case Analyzer analyze the case with the guidance of legal rule insights given criminal facts. Finally, the Legal Reasoner synthesizes the criminal facts, legal rule insights, and analysis results to derive the final decision. By conducting extensive experiments on a real-world dataset, experimental results validate the effectiveness of our proposed model. Our codes and dataset are available at https://anonymous.4open.science/r/CLEAR-3048.
%U https://aclanthology.org/2025.findings-emnlp.475/
%P 8937-8953
Markdown (Informal)
[CLEAR: A Framework Enabling Large Language Models to Discern Confusing Legal Paragraphs](https://aclanthology.org/2025.findings-emnlp.475/) (Xu et al., Findings 2025)
ACL