1 citations · 1 across the 7 of their papers we have counts for
4 papers · 1 filter
HERO-MoE: Historical Expert Routing with Scale-Preserving Fusion
Junxiang Qiu, Zhengsu Chen, Xinting Hu +6
Mixture-of-Experts (MoE) architectures have become a standard way to scale model capacity while keeping computation sparse, yet routing remains a key determinant of MoE quality and…
XPERT: Expert Knowledge Transfer for Effective Training of Language Models
Chang Liu, Boyu Shi, Xu Yang +1
Mixture-of-Experts (MoE) language models organize knowledge into explicitly routed expert modules, making expert-level representations traceable and analyzable. By analyzing expert…
Chain-based Distillation for Effective Initialization of Variable-Sized Small Language Models
Boyu Shi, YiCheng Jiang, Chang Liu +3
Large language models (LLMs) achieve strong performance but remain costly to deploy in resource-constrained settings. Training small language models (SLMs) from scratch is computat…
Understanding Performance Collapse in Layer-Pruned Large Language Models via Decision Representation Transitions
Boyu Shi, Chang Liu, ChuanBao Gao +2
Layer pruning efficiently reduces Large Language Model (LLM) computational costs but often triggers sudden performance collapse. Existing representation-based analyses struggle to…