Intent Prediction
Intent prediction focuses on accurately anticipating the goals and actions of individuals or agents, enabling proactive and efficient interactions in various contexts. Current research emphasizes leveraging multimodal data (e.g., visual, textual, physiological signals) and advanced machine learning models, including transformers, recurrent neural networks, and Bayesian frameworks, to improve prediction accuracy and interpretability. This field is crucial for advancing human-robot collaboration, autonomous systems, personalized recommendations, and other applications requiring anticipatory intelligence, ultimately leading to more seamless and efficient human-machine interactions.
Papers
Auto-Intent: Automated Intent Discovery and Self-Exploration for Large Language Model Web Agents
Jaekyeom Kim, Dong-Ki Kim, Lajanugen Logeswaran, Sungryull Sohn, Honglak Lee
Mobility-LLM: Learning Visiting Intentions and Travel Preferences from Human Mobility Data with Large Language Models
Letian Gong, Yan Lin, Xinyue Zhang, Yiwen Lu, Xuedi Han, Yichen Liu, Shengnan Guo, Youfang Lin, Huaiyu Wan