User Modeling Challenges in Interactive AI Assistant Systems
This work addresses the problem of personalization in AI assistants for users, but it appears incremental as it focuses on analyzing existing challenges and investigating model capabilities without claiming major breakthroughs.
The paper tackles the challenge of understanding users' mental states in interactive AI assistant systems to provide personalized guidance, analyzing task execution and investigating large language models' capabilities for interpreting user profiles.
Interactive Artificial Intelligent(AI) assistant systems are designed to offer timely guidance to help human users to complete a variety tasks. One of the remaining challenges is to understand user's mental states during the task for more personalized guidance. In this work, we analyze users' mental states during task executions and investigate the capabilities and challenges for large language models to interpret user profiles for more personalized user guidance.