From Experts to Sub-experts: Fine-grained Parameter-Efficient Fine-Tuning for MoE LLMs
As large language models (LLMs) scale rapidly, dense full-parameter adaptation becomes increasingly expensive, motivating sparse and modular architectures such as Mixture-of-Experts (MoE) models. This shift raises a key question for parameter-efficient fine-tuning (PEFT): at what granularity should parameters be select...
Zhentao Tan, Chang Liu, Yao Liu et al.
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