Dialogue Strategy

Dialogue strategy research focuses on designing and optimizing conversational agents' behavior to achieve specific goals, such as building rapport, eliciting information, or completing tasks. Current work leverages large language models (LLMs) and incorporates techniques like chain-of-thought reasoning, reinforcement learning, and data augmentation methods (e.g., path sampling in semantic space) to improve dialogue coherence, naturalness, and effectiveness. These advancements are improving human-computer interaction in various applications, including customer service, healthcare, and sales, by enabling more engaging and efficient conversations. The development of robust evaluation metrics for dialogue quality remains a key challenge.