Emotion-Aware Dialogue Generation

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5 papers in the last four weeks, up 67% on the four weeks before. 0.0% of all new papers.

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Reducing Conversational Escalation in Large Language Model Dialogue with Nonviolent Communication Constraints
May 1, 2026Zhixing Sun, Shenghe Xu, Tao LiLLM Prompting LLM Safety
Beijing University of Posts and Telecommunications Beijing, China · City University of Hong Kong Hong Kong SAR, China
Self-EmoQ: Plutchik-Guided Value-based Planning to Drive Streaming Emotional TTS
Apr 21, 2026Yue Zhao, Hongyan Li, Yong Chen +1TTS Synthesis Streaming TTS Synthesis
Geely AI Lab
A Multi-Agent Framework with Structured Reasoning and Reflective Refinement for Multimodal Empathetic Response Generation
Apr 21, 2026Liping Wang, Cheng Ye, Weidong Chen +3LLM Self-Refinement Multimodal Reasoning
School of Information Science and Technology, USTC Hefei, China
PRISMA: Preference-Reinforced Self-Training Approach for Interpretable Emotionally Intelligent Negotiation Dialogues
Apr 20, 2026Prajwal Vijay Kajare, Priyanshu Priya, Bikash Santra +1Automated Negotiation CoT Reasoning
School of Artificial Intelligence and Data Science, Indian Institute of Technology Jodhpur, India · Department of Computer Science and Engineering, Indian Institute of Technology Patna, India
FreezeEmpath: Efficient Training for Empathetic Spoken Chatbots with Frozen LLMs
Apr 20, 2026Yun Hong, Yan Zhou, Yang FengSpoken Dialogue Systems Efficient Language Model Training
Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences (ICT/CAS) · State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences · University of Chinese Academy of Sciences, Beijing, China
SELF-EMO: Emotional Self-Evolution from Recognition to Consistent Expression
Apr 20, 2026Shaowei Zhang, Faqiang Qian, Yan Chen +5RL for Language Models Emotion Recognition in Conversations
1SenseTime · 2Tsinghua University · 3Shanghai Jiao Tong University +1
Modeling Multiple Support Strategies within a Single Turn for Emotional Support Conversations
Apr 20, 2026Jie Zhu, Huaixia Dou, Junhui Li +5Emotional Support Conversation Emotion-Aware Dialogue Generation
School of Computer Science and Technology, Soochow University · Qwen DianJin Team, Alibaba Cloud Computing · Xiamen University
From Stateless to Situated: Building a Psychological World for LLM-Based Agents
Mar 26, 2026Boning Zhao, Yutong Hu, Xinnuo LiEmotional Support Conversation LLM Agents
Independent Researcher, Shanghai, China · New York University, New York, NY, USA

May 1, 2026, cs.CLJ/K move · Enter open · S save

Reducing Conversational Escalation in Large Language Model Dialogue with Nonviolent Communication Constraints

Zhixing Sun, Shenghe Xu, Tao Li

Beijing University of Posts and Telecommunications Beijing, China · City University of Hong Kong Hong Kong SAR, China

LLM Prompting LLM Safety Conversational Agents Emotion-Aware Dialogue Generation

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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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