Latent Intent

Latent intent research focuses on uncovering hidden user goals or objectives from observed data, such as text, actions, or sensor readings, aiming to improve human-computer interaction and decision-making systems. Current research employs various techniques, including deep learning models for clustering and representation learning, reinforcement learning frameworks to model and influence human behavior, and methods leveraging large language models for abstractive summarization and intent discovery. This work has significant implications for diverse applications, including personalized recommendations, human-robot collaboration, and the development of more intuitive and effective conversational AI systems.