Summary of Svfit: Parameter-efficient Fine-tuning Of Large Pre-trained Models Using Singular Values, by Chengwei Sun et al.
SVFit: Parameter-Efficient Fine-Tuning of Large Pre-Trained Models Using Singular Values
by Chengwei Sun, Jiwei Wei, Yujia Wu, Yiming Shi, Shiyuan He, Zeyu Ma, Ning Xie, Yang Yang
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper presents a novel parameter-efficient fine-tuning (PEFT) approach called SVFit for large pre-trained models (LPMs). The existing PEFT methods, such as LoRA, have limitations in terms of random initialization for low-rank matrices, which can lead to inefficiencies in gradient descent and diminished generalizability. To address these issues, SVFit leverages singular value decomposition (SVD) to initialize low-rank matrices using critical singular values as trainable parameters. The approach involves performing 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. The proposed method is evaluated across various pre-trained models in natural language understanding, text-to-image generation, and image classification tasks, demonstrating improved performance while requiring 16 times fewer trainable parameters than LoRA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SVFit is a new way to make big AI models smaller and better at learning new things. Right now, these models are very good at doing lots of tasks like understanding language and recognizing pictures. However, they take up a lot of computer memory and can be slow to learn new things. To fix this, SVFit uses a special math technique called SVD to make the model smaller while still keeping most of its power. This makes it faster and better at learning new things. The researchers tested SVFit on lots of different AI tasks and found that it worked really well, even with much less computer memory than before. |
Keywords
» Artificial intelligence » Domain adaptation » Fine tuning » Gradient descent » Image classification » Image generation » Language understanding » Lora » Parameter efficient
