Reducing Conversational Escalation in Large Language Model Dialogue with Nonviolent Communication Constraints
For developers of LLMs used in emotionally charged settings, this work provides a simple method to improve trustworthiness by reducing conflict escalation.
The paper introduces lightweight prompt-level constraints based on Nonviolent Communication (NVC) to reduce conversational escalation in LLM dialogue, showing consistent reductions in escalation and stabilization of interactions with highly resistant users across multiple models.
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.