18 citations · 18 across the 5 of their papers we have counts for
5 papers
BehaveGPT: A Foundation Model for Large-scale User Behavior Modeling
Jiahui Gong, Jingtao Ding, Fanjin Meng +5
In recent years, foundational models have revolutionized the fields of language and vision, demonstrating remarkable abilities in understanding and generating complex data; however…
Large language model as user daily behavior data generator: balancing population diversity and individual personality
Haoxin Li, Jingtao Ding, Jiahui Gong +1
Predicting human daily behavior is challenging due to the complexity of routine patterns and short-term fluctuations. While data-driven models have improved behavior prediction by…
Tuning Language Models for Robust Prediction of Diverse User Behaviors
Fanjin Meng, Jingtao Ding, Jiahui Gong +5
Predicting user behavior is essential for intelligent assistant services, yet deep learning models often struggle to capture long-tailed behaviors. Large language models (LLMs), wi…
Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models
Fengli Xu, Qianyue Hao, Zefang Zong +17
Language has long been conceived as an essential tool for human reasoning. The breakthrough of Large Language Models (LLMs) has sparked significant research interest in leveraging…
A Population-to-individual Tuning Framework for Adapting Pretrained LM to On-device User Intent Prediction
Jiahui Gong, Jingtao Ding, Fanjin Meng +5
Mobile devices, especially smartphones, can support rich functions and have developed into indispensable tools in daily life. With the rise of generative AI services, smartphones c…