SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors

• URL: http://arxiv.org/abs/2405.19597v1
• Date: Thu, 30 May 2024 01:27:43 GMT
• Title: SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors
• Authors: Vijay Lingam, Atula Tejaswi, Aditya Vavre, Aneesh Shetty, Gautham Krishna Gudur, Joydeep Ghosh, Alex Dimakis, Eunsol Choi, Aleksandar Bojchevski, Sujay Sanghavi,
• Abstract summary: We propose SVFT, a simple approach that fundamentally differs from existing methods. SVFT updates (W) as a sparse combination of outer products of its singular vectors, training only the coefficients (scales) of these sparse combinations. Experiments on language and vision benchmarks show that SVFT recovers up to 96% of full fine-tuning performance while training only 0.006 to 0.25% of parameters.
• Score: 80.6043267994434
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Popular parameter-efficient fine-tuning (PEFT) methods, such as LoRA and its variants, freeze pre-trained model weights \(W\) and inject learnable matrices \(\Delta W\). These \(\Delta W\) matrices are structured for efficient parameterization, often using techniques like low-rank approximations or scaling vectors. However, these methods typically show a performance gap compared to full fine-tuning. Although recent PEFT methods have narrowed this gap, they do so at the cost of additional learnable parameters. We propose SVFT, a simple approach that fundamentally differs from existing methods: the structure imposed on \(\Delta W\) depends on the specific weight matrix \(W\). Specifically, SVFT updates \(W\) as a sparse combination of outer products of its singular vectors, training only the coefficients (scales) of these sparse combinations. This approach allows fine-grained control over expressivity through the number of coefficients. Extensive experiments on language and vision benchmarks show that SVFT recovers up to 96% of full fine-tuning performance while training only 0.006 to 0.25% of parameters, outperforming existing methods that only recover up to 85% performance using 0.03 to 0.8% of the trainable parameter budget.

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