Conversation Dynamic

Conversation dynamics research focuses on understanding how interactions unfold, aiming to improve the quality and safety of human-human and human-AI conversations. Current research employs various approaches, including graph convolutional networks to model user interactions and predict derailment, large language models to generate synthetic dialogues for analysis and intervention testing, and transformer-based models to analyze conversational flow and predict user ratings. This field is significant for advancing human-computer interaction, improving online safety through toxicity detection and intervention, and developing more natural and engaging conversational AI systems across diverse applications.