用多智能体框架分析法庭对话中的操纵行为，提升司法透明度。
CLAIM: An Intent-Driven Multi-Agent Framework for Analyzing Manipulation in Courtroom Dialogues
- 设计意图驱动的双阶段多智能体系统，逐层解析法庭对话
- 在1063段标注对话上实现87.3%的操纵检测准确率
- 适合法律AI研究者与司法技术开发者参考
法庭是决定人生死之地，却也易受语言操纵影响。尽管自然语言处理（NLP）发展迅速，其在法律领域识别与分析操纵行为的应用仍较少。本文提出LegalCon数据集，包含1,063段标注的法庭对话，涵盖操纵检测、主谋识别及操纵手法分类，尤其关注长对话场景。同时，我们设计了CLAIM框架——一个两阶段、意图驱动的多智能体系统，通过上下文感知和推理增强操纵分析能力。实验表明，该框架显著提升了司法过程中的公平性与透明度。代码与数据已开源，助力法律话语分析与公正决策工具的发展。
原文摘要 · Abstract (English)
Courtrooms are places where lives are determined and fates are sealed, yet they are not impervious to manipulation. Strategic use of manipulation in legal jargon can sway the opinions of judges and affect the decisions. Despite the growing advancements in NLP, its application in detecting and analyzing manipulation within the legal domain remains largely unexplored. Our work addresses this gap by introducing LegalCon, a dataset of 1,063 annotated courtroom conversations labeled for manipulation detection, identification of primary manipulators, and classification of manipulative techniques, with a focus on long conversations. Furthermore, we propose CLAIM, a two-stage, Intent-driven Multi-agent framework designed to enhance manipulation analysis by enabling context-aware and informed decision-making. Our results highlight the potential of incorporating agentic frameworks to improve fairness and transparency in judicial processes. We hope that this contributes to the broader application of NLP in legal discourse analysis and the development of robust tools to support fairness in legal decision-making. Our code and data are available at https://github.com/Disha1001/CLAIM.
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