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Multi-Agent Simulator Drives Language Models for Legal Intensive Interaction

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arxiv 2502.06882 v1 pith:7HAOEYID submitted 2025-02-08 cs.CL cs.AI

Shengbin Yue , Ting Huang , Zheng Jia , Siyuan Wang , Shujun Liu , Yun Song , Xuanjing Huang , Zhongyu Wei This is my paper
classification cs.CLcs.AI
keywords legalinteractivescenariosdataintroduceslanguagellmsmaser
verification ladder T0 review T1 audit T2 compute T3 formal
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Large Language Models (LLMs) have significantly advanced legal intelligence, but the scarcity of scenario data impedes the progress toward interactive legal scenarios. This paper introduces a Multi-agent Legal Simulation Driver (MASER) to scalably generate synthetic data by simulating interactive legal scenarios. Leveraging real-legal case sources, MASER ensures the consistency of legal attributes between participants and introduces a supervisory mechanism to align participants' characters and behaviors as well as addressing distractions. A Multi-stage Interactive Legal Evaluation (MILE) benchmark is further constructed to evaluate LLMs' performance in dynamic legal scenarios. Extensive experiments confirm the effectiveness of our framework.

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