ShadowPEFT replaces distributed low-rank weight perturbations with a centralized, depth-shared shadow module that evolves parallel hidden states layer by layer, matching or beating LoRA and DoRA on generation and understanding tasks at similar parameter budgets.
Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning.Advances in Neural Information Processing Systems, 35:1950–1965, 2022a
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ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning
ShadowPEFT replaces distributed low-rank weight perturbations with a centralized, depth-shared shadow module that evolves parallel hidden states layer by layer, matching or beating LoRA and DoRA on generation and understanding tasks at similar parameter budgets.