Year: 2025
Type: conference-paper
Abstract: With the rapid advancement of large language models (LLMs), intelligent conversational assistants have demonstrated remarkable capabilities across various domains. However, they still mainly rely on explicit textual input and do not know the real world behaviors of users. This paper proposes a context-sensitive conversational assistant framework grounded in mobile sensing data. By collecting user behavior and environmental data through smartphones, we abstract these signals into 16 contextual scenarios and translate them into natural language prompts, thus improving the model's understanding of the user's state. We design a structured prompting system to guide the LLM in generating a more personalized and contextually relevant dialogue. This approach integrates mobile sensing with large language models, demonstrating the potential of passive behavioral data in intelligent conversation and offering a viable path toward digital health and personalized interaction.
Authors: Ziyan Zhang, Nan Gao, Zhiqiang Nie, Shantanu Pal, Haining Zhang
Institutions: Nankai University, Tsinghua University, Deakin University
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7117456159