发表机构
University of Science and Technology of China; Singapore Management University; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（中国科学技术大学; 新加坡管理大学; 合肥综合性国家科学中心人工智能研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出ProMediConv基准框架，将法律纠纷调解建模为主动多阶段对话过程，整合11种调解策略和四种当事人行为状态，构建高保真数据集并引入MAD细粒度指标，系统评估现有模型能力，为AI辅助冲突解决提供量化标准。
AI 中文摘要
纠纷调解对于维护社会和谐与韧性至关重要，然而培养熟练的调解员成本高昂且耗时。现有的基于大语言模型的调解研究仍受限于不切实际的任务设定、低保真度的数据集以及粗糙的评估指标，这些指标掩盖了逐轮对话的动态变化。为弥补这些不足，我们提出了ProMediConv，一个新颖的基准测试框架，将调解建模为一个主动的、多阶段的、考虑当事人状态的对话过程，整合了11种调解策略和四种当事人行为模式（BP）状态。利用972个完整的真实世界案例，我们构建了一个高保真度的调解数据集，并带有话语级别的策略和BP状态标注。此外，为更好地评估智能体的影响，我们提出了MAD（平均属性差异），一种细粒度的指标，用于捕捉整个对话过程中的BP转变。借助该框架，我们通过评估多种模型以及我们定制的基线ProMediAgent，建立了一个全面的基准。广泛的实证分析揭示了关键的行为现象，并强调了当前模型在动态、多方调解中面临的持续挑战。最终，ProMediConv为推进AI辅助的冲突解决提供了严谨的基础和至关重要的量化标准。我们的数据集和代码库可在该https链接获取。
英文摘要
Dispute mediation is essential for maintaining social harmony and resilience, yet developing skilled mediators is costly and time-consuming. Existing LLM-based mediation research remains limited by unrealistic task formulations, low-fidelity datasets, and coarse evaluation metrics that obscure turn-by-turn dynamics. To address these gaps, we introduce ProMediConv, a novel benchmarking framework that models mediation as a proactive, multi-stage, and party-aware dialogue process incorporating 11 mediation strategies and four party behavior pattern (BP) states. Using 972 complete real-world cases, we construct a high-fidelity mediation dataset with utterance-level annotations of strategies and BP states. Furthermore, to better assess agent impact, we propose MAD (Mean Attribute Difference), a fine-grained metric that captures BP shifts throughout the dialogue. Leveraging this framework, we establish a comprehensive benchmark by evaluating diverse models alongside our tailored baseline ProMediAgent. Extensive empirical analyses reveal critical behavioral phenomena and underscore the persistent challenges current models face in dynamic, multi-party mediation. Ultimately, ProMediConv provides a rigorous foundation and a vital quantitative standard for advancing AI-assisted conflict resolution. Our dataset and codebase are accessible at https://github.com/ZsWei66/ProMediConv_repo.
CommentsAccepted to Findings of EMNLP2026
