4 papers
CBD: API-Only LLM Black-Box Unlearning through Controlled Behavioral Divergence
Zhiqiang Xie, Yijing Lin, Zhipeng Gao +1
Edge devices increasingly invoke large language models (LLMs) through API services for context aware edge intelligence, while edge generated data may be collected to improve LLMs a…
U-MASK: User-adaptive Spatio-Temporal Masking for Personalized Mobile AI Applications
Shiyuan Zhang, Yilai Liu, Yuwei Du +3
Personalized mobile artificial intelligence applications are widely deployed, yet they are expected to infer user behavior from sparse and irregular histories under a continuously…
Experience Scaling: Post-Deployment Evolution For Large Language Models
Xingkun Yin, Kaibin Huang, Dong In Kim +1
Scaling model size, training data, and compute power have driven advances in large language models (LLMs), but these approaches are reaching saturation as human-generated text is e…
Empowering Intelligent Low-altitude Economy with Large AI Model Deployment
Zhonghao Lyu, Yulan Gao, Junting Chen +4
Low-altitude economy (LAE) represents an emerging economic paradigm that redefines commercial and social aerial activities. Large artificial intelligence models (LAIMs) offer trans…