Multi-Agent Simulator Drives Language Models for Legal Intensive Interaction

• URL: http://arxiv.org/abs/2502.06882v1
• Date: Sat, 08 Feb 2025 15:05:24 GMT
• Title: Multi-Agent Simulator Drives Language Models for Legal Intensive Interaction
• Authors: Shengbin Yue, Ting Huang, Zheng Jia, Siyuan Wang, Shujun Liu, Yun Song, Xuanjing Huang, Zhongyu Wei,
• Abstract summary: This paper introduces a Multi-agent Legal Simulation Driver (MASER) to scalably generate synthetic data by simulating interactive legal scenarios. MASER ensures the consistency of legal attributes between participants and introduces a supervisory mechanism to align participants' characters and behaviors.
• Score: 37.856194200684364
• License:
• Abstract: 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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