用非暴力沟通原则让大模型对话更平和，减少冲突升级。
Reducing Conversational Escalation in Large Language Model Dialogue with Nonviolent Communication Constraints
- 通过非暴力沟通原则设计轻量提示约束
- 在多轮对话中显著降低冲突升级概率
- 适合高对抗场景下的智能客服与心理支持
大型语言模型越来越多地应用于涉及人际冲突、挫败感和痛苦情绪的场景。尽管以往的安全研究聚焦于防止毒性或违规内容，但对可能无意中加剧冲突的对话行为关注较少。本文探讨是否可通过基于非暴力沟通（NVC）的轻量级提示约束，引导大模型采取更具化解作用的对话行为。我们将NVC原则转化为过程导向的指导规则：避免指责、关注用户情感体验、先澄清再建议。在多个指令微调模型与不同用户抵抗水平下，采用双代理仿真框架进行测试，结果表明，使用NVC约束的提示能持续降低对话升级，并稳定与高抵抗用户的交互。这表明，简单的沟通约束可显著提升大模型在易冲突场景中的可信度。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly used in emotionally charged situations involving interpersonal conflict, frustration, and distress. While prior safety research has focused on preventing explicit harms such as toxic or policy-violating content, less attention has been paid to conversational behaviors that may unintentionally escalate conflict. In this paper, we investigate whether LLMs can be guided toward more de-escalating dialogue behavior through lightweight prompt-level constraints derived from Nonviolent Communication (NVC). We reformulate NVC principles as process-oriented guidelines that discourage blame attribution, emphasize attention to users' emotional experiences, and encourage clarification before advice. Using a dual-agent simulation framework across multiple instruction-tuned models and user resistance levels, we show that NVC-constrained prompting consistently reduces conversational escalation and stabilizes interactions with highly resistant users. These results suggest that simple communication constraints can meaningfully improve the trustworthiness of LLM dialogue in conflict-prone settings.
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