Large pre-trained models, such as large language models (LLMs), present significant resource challenges for fine-tuning due to their extensive parameter sizes, especially for applications in mobile systems. To address this, Low-Rank Adaptation (LoRA) has been developed to reduce resource consumption while maintaining satisfactory fine-tuning results. Despite its effectiveness, the original LoRA method faces challenges of suboptimal performance and overfitting. This paper investigates the intrinsic dimension of the matrix updates approximated by the LoRA method and reveals the performance benefits of increasing this intrinsic dimension. By employing regularization and a gradient masking method that encourages higher intrinsic dimension, the proposed method, termed Regularized and Masked LoRA (RM-LoRA), achieves superior generalization performance with the same or lower trainable parameter budget compared to the original LoRA and its latest variants across various open-source vision and language datasets.

通过研究矩阵更新的内在维度，揭示了提高内在维度在降低资源消耗、优化性能方面的潜在好处，并通过正则化和梯度屏蔽方法设计了Regularized and Masked LoRA (RM-LoRA)方法，相对于原始的LoRA方法和其最新变体，在各种开源视觉和语言数据集上实现了卓越的泛化性能。

大规模模型的参数效率和泛化能力增强：一种正则化和遮蔽低秩适应方法