4 citations · 5 across the 5 of their papers we have counts for
5 papers
Do LLMs Need to See Everything? A Benchmark and Study of Failures in LLM-driven Smartphone Automation using Screentext vs. Screenshots
Shiquan Zhang, Tianyi Zhang, Le Fang +3
With the rapid advancement of large language models (LLMs), mobile agents have emerged as promising tools for phone automation, simulating human interactions on screens to accompli…
Raising Awareness of Location Information Vulnerabilities in Social Media Photos using LLMs
Ying Ma, Shiquan Zhang, Dongju Yang +3
Location privacy leaks can lead to unauthorised tracking, identity theft, and targeted attacks, compromising personal security and privacy. This study explores LLM-powered location…
AutoJournaling: A Context-Aware Journaling System Leveraging MLLMs on Smartphone Screenshots
Tianyi Zhang, Shiquan Zhang, Le Fang +3
Journaling offers significant benefits, including fostering self-reflection, enhancing writing skills, and aiding in mood monitoring. However, many people abandon the practice beca…
ScreenTK: Seamless Detection of Time-Killing Moments Using Continuous Mobile Screen Text and On-Device LLMs
Le Fang, Shiquan Zhang, Hong Jia +2
Smartphones have become essential to people's digital lives, providing a continuous stream of information and connectivity. However, this constant flow can lead to moments where us…
Enabling On-Device LLMs Personalization with Smartphone Sensing
Shiquan Zhang, Ying Ma, Le Fang +3
This demo presents a novel end-to-end framework that combines on-device large language models (LLMs) with smartphone sensing technologies to achieve context-aware and personalized…