Summary of On the Implicit Relation Between Low-rank Adaptation and Differential Privacy, by Saber Malekmohammadi et al.
On the Implicit Relation Between Low-Rank Adaptation and Differential Privacy
by Saber Malekmohammadi, Golnoosh Farnadi
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper delves into the realm of natural language processing, focusing on large-scale pre-training models and their adaptation to specific tasks or domains. The authors propose an innovative approach to address the impracticality of full fine-tuning as model sizes grow. Low-rank task adaptation methods, such as LoRA and FLoRA, are introduced, which keep pre-trained model weights fixed while incorporating trainable low-rank decomposition matrices into transformer architecture adapters. This approach significantly reduces trainable parameters required for downstream tasks compared to full fine-tuning. The paper explores the lens of data privacy in low-rank adaptation, theoretically showing that it’s equivalent to injecting random noise into batch gradients w.r.t adapter parameters. Quantifying variance and establishing a Berry-Esseen type bound on total variation distance between injected noise and Gaussian distribution with same variance, the authors demonstrate that low-rank adaptation dynamics is close to differentially private fine-tuning of adapters. Finally, using Johnson-Lindenstrauss lemma, they show that augmented low-rank adaptation is very close to performing DPSGD algorithm with fixed noise scale to fine-tune adapters. These findings suggest that low-rank adaptation provides privacy w.r.t the fine-tuning data implicitly. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how to make big language models work better for specific tasks or areas of study, like teaching machines to understand human languages. The problem is that these huge models take too long to adjust to new tasks when they’re so big. To solve this, some experts have developed a way to keep the main model’s settings unchanged and add small adjustments to help it learn faster. This method reduces the number of things the machine needs to remember while still helping it do its job better. The paper looks at how this approach affects data privacy when we’re teaching machines to learn from private information. It shows that this method is like adding a bit of random noise to what the machine learns, which helps protect people’s private data. |
Keywords
» Artificial intelligence » Fine tuning » Lora » Low rank adaptation » Natural language processing » Transformer
