Diverse Response

Diverse response generation in conversational AI aims to create more engaging and natural-sounding interactions by producing a variety of appropriate replies, rather than relying on repetitive or generic outputs. Current research focuses on leveraging large language models (LLMs) and incorporating techniques like self-bootstrapping, contrastive learning, and explicit modeling of common ground or user emotional states to achieve this diversity. These advancements are improving the quality and realism of chatbot interactions, with implications for applications ranging from customer service and mental health support to more effective human-computer collaboration.