New method for agent dialogue-based data synthesis

Agents require large amounts of data to learn through dialogue. Space-evolutionary data synthesis using generative models raises data quality and diversity in closed-loop training, and continuously improves the performance of conversational agents. It may reduce the data-creation burden in practical applications.

Why it mattersIt addresses data shortages faced when deploying conversational agents in real-world settings and improves learning efficiency.