22 citations · 22 across the 3 of their papers we have counts for
3 papers
cs.DC2025
Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices
Tao Shen, Didi Zhu, Ziyu Zhao +3
The remarkable success of foundation models has been driven by scaling laws, demonstrating that model performance improves predictably with increased training data and model size.…
cs.CL2025
Rewrite to Jailbreak: Discover Learnable and Transferable Implicit Harmfulness Instruction
Yuting Huang, Chengyuan Liu, Yifeng Feng +4
As Large Language Models (LLMs) are widely applied in various domains, the safety of LLMs is increasingly attracting attention to avoid their powerful capabilities being misused. E…
cs.LG2021★ 22 cited
Federated Graph Learning -- A Position Paper
Huanding Zhang, Tao Shen, Fei Wu +3
Graph neural networks (GNN) have been successful in many fields, and derived various researches and applications in real industries. However, in some privacy sensitive scenarios (l…