2 citations · 2 across the 13 of their papers we have counts for
6 papers · 1 filter
CORE: Reducing UI Exposure in Mobile Agents via Collaboration Between Cloud and Local LLMs
Gucongcong Fan, Chaoyue Niu, Chengfei Lyu +2
Mobile agents rely on Large Language Models (LLMs) to plan and execute tasks on smartphone user interfaces (UIs). While cloud-based LLMs achieve high task accuracy, they require up…
MNN-AECS: Energy Optimization for LLM Decoding on Mobile Devices via Adaptive Core Selection
Zhengxiang Huang, Chaoyue Niu, Zhaode Wang +8
As the demand for on-device Large Language Model (LLM) inference grows, energy efficiency has become a major concern, especially for battery-limited mobile devices. Our analysis sh…
RAGRouter: Learning to Route Queries to Multiple Retrieval-Augmented Language Models
Jiarui Zhang, Xiangyu Liu, Yong Hu +3
Retrieval-Augmented Generation (RAG) significantly improves the performance of Large Language Models (LLMs) on knowledge-intensive tasks. However, varying response quality across L…
Automated Privacy Information Annotation in Large Language Model Interactions
Hang Zeng, Xiangyu Liu, Yong Hu +4
Users interacting with large language models (LLMs) under their real identifiers often unknowingly risk disclosing private information. Automatically notifying users whether their…
Collaborative Learning of On-Device Small Model and Cloud-Based Large Model: Advances and Future Directions
Chaoyue Niu, Yucheng Ding, Junhui Lu +7
The conventional cloud-based large model learning framework is increasingly constrained by latency, cost, personalization, and privacy concerns. In this survey, we explore an emerg…
Personalized Language Model Learning on Text Data Without User Identifiers
Yucheng Ding, Yangwenjian Tan, Xiangyu Liu +6
In many practical natural language applications, user data are highly sensitive, requiring anonymous uploads of text data from mobile devices to the cloud without user identifiers.…