@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://aclanthology.org/2025.findings-naacl.365/",
doi = "10.18653/v1/2025.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."
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<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.</abstract>
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%0 Conference Proceedings
%T Multi-Agent Simulator Drives Language Models for Legal Intensive Interaction
%A Yue, Shengbin
%A Huang, Ting
%A Jia, Zheng
%A Wang, Siyuan
%A Liu, Shujun
%A Song, Yun
%A Huang, Xuanjing
%A Wei, Zhongyu
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F shengbinyue-etal-2025-multi
%X 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.
%R 10.18653/v1/2025.findings-naacl.365
%U https://aclanthology.org/2025.findings-naacl.365/
%U https://doi.org/10.18653/v1/2025.findings-naacl.365
%P 6537-6570
Markdown (Informal)
[Multi-Agent Simulator Drives Language Models for Legal Intensive Interaction](https://aclanthology.org/2025.findings-naacl.365/) (Yue et al., Findings 2025)
ACL
- Shengbin Yue, Ting Huang, Zheng Jia, Siyuan Wang, Shujun Liu, Yun Song, Xuanjing Huang, and Zhongyu Wei. 2025. Multi-Agent Simulator Drives Language Models for Legal Intensive Interaction. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 6537–6570, Albuquerque, New Mexico. Association for Computational Linguistics.