SVFit: Parameter-Efficient Fine-Tuning of Large Pre-Trained Models Using Singular Values

• URL: http://arxiv.org/abs/2409.05926v1
• Date: Mon, 9 Sep 2024 08:44:53 GMT
• Title: SVFit: Parameter-Efficient Fine-Tuning of Large Pre-Trained Models Using Singular Values
• Authors: Chengwei Sun, Jiwei Wei, Yujia Wu, Yiming Shi, Shiyuan He, Zeyu Ma, Ning Xie, Yang Yang,
• Abstract summary: Large pre-trained models (LPMs) have demonstrated exceptional performance in diverse natural language processing and computer vision tasks. fully fine-tuning these models poses substantial memory challenges, particularly in resource-constrained environments. We propose SVFit, a novel PEFT approach that leverages singular value decomposition (SVD) to low-rank matrices using critical singular values as trainable parameters.
• Score: 12.137869917556415
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large pre-trained models (LPMs) have demonstrated exceptional performance in diverse natural language processing and computer vision tasks. However, fully fine-tuning these models poses substantial memory challenges, particularly in resource-constrained environments. Parameter-efficient fine-tuning (PEFT) methods, such as LoRA, mitigate this issue by adjusting only a small subset of parameters. Nevertheless, these methods typically employ random initialization for low-rank matrices, which can lead to inefficiencies in gradient descent and diminished generalizability due to suboptimal starting points. To address these limitations, we propose SVFit, a novel PEFT approach that leverages singular value decomposition (SVD) to initialize low-rank matrices using critical singular values as trainable parameters. Specifically, SVFit performs SVD on the pre-trained weight matrix to obtain the best rank-r approximation matrix, emphasizing the most critical singular values that capture over 99% of the matrix's information. These top-r singular values are then used as trainable parameters to scale the fundamental subspaces of the matrix, facilitating rapid domain adaptation. Extensive experiments across various pre-trained models in natural language understanding, text-to-image generation, and image classification tasks reveal that SVFit outperforms LoRA while requiring 16 times fewer trainable parameters.

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