Conversation Generation

Conversation generation research aims to create realistic and engaging synthetic dialogues across diverse domains, driven by the need for large, high-quality datasets to train conversational AI systems. Current efforts leverage large language models (LLMs), often incorporating techniques like chain-of-thought prompting and iterative refinement, to generate dialogues grounded in multiple documents, reflecting various personas, emotions, and social norms. This work is significant because it addresses the scarcity of real-world conversational data, enabling advancements in applications ranging from medical dialogue systems to more human-like chatbots and virtual avatars.