使用非暴力沟通约束减少大语言模型对话中的对话升级
Reducing Conversational Escalation in Large Language Model Dialogue with Nonviolent Communication Constraints
AI总结：
本研究通过非暴力沟通原则的轻量级提示约束，引导大语言模型在冲突对话中减少升级行为，实验表明该方法能稳定与高度抵抗用户的交互。
摘要：
大语言模型（LLMs）越来越多地用于涉及人际冲突、沮丧和痛苦的情绪化情境。虽然先前的安全研究侧重于防止显性危害，如有毒或违反政策的内容，但较少关注可能无意中加剧冲突的对话行为。在本文中，我们研究了是否可以通过源自非暴力沟通（NVC）的轻量级提示约束，引导LLMs采取更缓和的对话行为。我们将NVC原则重新表述为面向过程的指导方针，这些方针不鼓励归咎责任，强调关注用户的情感体验，并鼓励在提供建议前先进行澄清。使用一个双智能体模拟框架，在多种指令调优模型和用户抵抗水平下，我们表明NVC约束的提示一致地减少了对话升级，并稳定了与高度抵抗用户的交互。这些结果表明，简单的沟通约束可以显著提高LLM对话在易冲突环境中的可信度。
英文摘要：
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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