1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.CL2026
Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory
Hanzuo Liu, Xuan Qi, Chunyu Liu +6
Transformer depth is not used uniformly: lower and middle layers build semantic representations, while upper layers increasingly specialize them for prediction. We turn this divisi…
cs.LG2024★ 1 cited
FastSwitch: Optimizing Context Switching Efficiency in Fairness-aware Large Language Model Serving
Ao Shen, Zhiyao Li, Mingyu Gao
Serving numerous users and requests concurrently requires good fairness in Large Language Models (LLMs) serving system. This ensures that, at the same cost, the system can meet the…