In linear regression, LoRA can achieve lower excess risk than full fine-tuning when the pretraining-downstream difference is low-rank, and small LoRA ranks can improve generalization by acting as regularization.
PaLM: Scaling language modeling with pathways.Journal of Machine Learning Research, 24(240): 1–113, 2023
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LoRA vs. Full Fine-Tuning: A Theoretical Perspective
In linear regression, LoRA can achieve lower excess risk than full fine-tuning when the pretraining-downstream difference is low-rank, and small LoRA ranks can improve generalization by acting as regularization.