ProMediConv: Benchmarking Proactive Conversational Agents in Legal Dispute Mediation
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 a...
| Authors | Zesheng Wei, Mengfan Li, Wenhao Liu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 10 September 2026 |
| Last updated at source | 10 September 2026 |
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