@inproceedings{shengbinyue-etal-2025-multi,
    title = "Multi-Agent Simulator Drives Language Models for Legal Intensive Interaction",
    author = "Yue, Shengbin  and
      Huang, Ting  and
      Jia, Zheng  and
      Wang, Siyuan  and
      Liu, Shujun  and
      Song, Yun  and
      Huang, Xuanjing  and
      Wei, Zhongyu",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.findings-naacl.365/",
    pages = "6537--6570",
    ISBN = "979-8-89176-195-7",
    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."
}