7 papers
Rewiring Experts on the Fly:Continuous Rerouting for Better Online Adaptation in Mixture-of-Expert models
Guinan Su, Yanwu Yang, Li Shen +3
Mixture-of-Experts (MoE) models achieve efficient scaling through sparse expert activation, but often suffer from suboptimal routing decisions due to distribution shifts in deploym…
Demystifying the Roles of LLM Layers in Retrieval, Knowledge, and Reasoning
Xinyuan Song, Keyu Wang, PengXiang Li +2
Recent studies suggest that the deeper layers of Large Language Models (LLMs) contribute little to representation learning and can often be removed without significant performance…
Model Unmerging: Making Your Models Unmergeable for Secure Model Sharing
Zihao Wang, Enneng Yang, Lu Yin +2
Model merging leverages multiple finetuned expert models to construct a multi-task model with low cost, and is gaining increasing attention. However, as a growing number of finetun…
GPTailor: Large Language Model Pruning Through Layer Cutting and Stitching
Guinan Su, Li Shen, Lu Yin +3
Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. However, such impressive capability typically comes with a substantial mod…
Double-Checker: Enhancing Reasoning of Slow-Thinking LLMs via Self-Critical Fine-Tuning
Xin Xu, Tianhao Chen, Fan Zhang +11
While slow-thinking large language models (LLMs) exhibit reflection-like reasoning, commonly referred to as the "aha moment:, their ability to generate informative critiques and re…
AlphaDecay: Module-wise Weight Decay for Heavy-Tailed Balancing in LLMs
Di He, Songjun Tu, Ajay Jaiswal +4
Weight decay is a standard regularization technique for training large language models (LLMs). While it is common to assign a uniform decay rate to every layer, this approach overl…