CC-Mediation: Evaluating Large Language Models for Cross-Cultural Conflict Mediation

2026-09-04

AI Summary

研究通过创建CC-Mediation数据集和提出新的评估指标，解决了大型语言模型在跨文化冲突调解中何时干预及如何响应的问题。

Abstract

Cross-cultural mediation by large language models (LLMs) requires deciding both when to intervene and how to respond in culturally grounded conflicts. Progress on this problem has been limited by the lack of (1) mediation datasets with measurable downstream effects and (2) principled metrics for evaluating intercultural stance change. To address these gaps, we introduce CC-Mediation, a cross-cultural mediation benchmark of $1{,}661$ ten-turn dialogues grounded in the Developmental Model of Intercultural Sensitivity (DMIS), containing culturally grounded conflicts, mediation interventions, and post-intervention trajectories. We further propose two DMIS-based evaluation metrics: Trajectory AUC, which measures the persistence of intercultural improvement over time, and a signed Wasserstein-1 distance, which measures the magnitude and direction of shifts in intercultural stance. Both metrics show strong agreement with human judgment of DMIS-grounded stance shift. Using CC-Mediation, we find that current LLMs have limitations on both axes: intervention timing (when) failure stems from a positional prior that ignores dialogue content, while mediation strategy (how) failure arises from a late-layer elicitation collapse rather than a knowledge deficit.

Problem

• cross-cultural mediation
• large language models
• mediation datasets
• evaluation metrics
• intercultural stance change

Innovation

• CC-Mediation
• Trajectory AUC
• signed Wasserstein-1 distance