Summary of Lora-pro: Are Low-rank Adapters Properly Optimized?, by Zhengbo Wang et al.
LoRA-Pro: Are Low-Rank Adapters Properly Optimized?
by Zhengbo Wang, Jian Liang, Ran He, Zilei Wang, Tieniu Tan
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| 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 paper presents LoRA-Pro, a method that improves the performance of low-rank adaptation (LoRA) by adjusting the gradients of the two low-rank matrices in LoRA. This adjustment allows the low-rank gradient to more accurately approximate the full fine-tuning gradient, narrowing the performance gap between LoRA and full fine-tuning. The paper also theoretically derives the optimal solutions for adjusting the gradients and applies them during fine-tuning in LoRA-Pro. Experiments across various tasks, including natural language understanding, dialogue generation, and image classification, demonstrate that LoRA-Pro substantially improves LoRA’s performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LoRA-Pro is a new way to make machine learning models better without using too much computer power. It helps fix a problem called low-rank adaptation, which makes models less good than they could be. The team behind this method discovered that there’s a special connection between how it works and the way full fine-tuning works. They used this discovery to create LoRA-Pro, which can make models better by adjusting some important numbers. This new method was tested on many different tasks and showed big improvements. |
Keywords
» Artificial intelligence » Fine tuning » Image classification » Language understanding » Lora » Low rank adaptation » Machine learning
