Summary of Pace: Marrying Generalization in Parameter-efficient Fine-tuning with Consistency Regularization, by Yao Ni et al.
PACE: Marrying generalization in PArameter-efficient fine-tuning with Consistency rEgularization
by Yao Ni, Shan Zhang, Piotr Koniusz
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 proposes a novel approach to fine-tuning pre-trained transformers, called Parameter-Efficient Fine-Tuning (PEFT), which adapts the models to downstream tasks while retaining generalizability. The authors theoretically connect smaller weight gradient norms during training and larger datasets to improvements in model generalization. They introduce PACE, a method that marries PEFT with Consistency Regularization, which perturbs features learned from adapters with multiplicative noise to ensure fine-tuned models remain consistent under different perturbations. Theoretical analysis shows that PACE regularizes gradients for enhanced generalization and aligns the fine-tuned and pre-trained models to retain knowledge. Experimental evidence supports these theories, demonstrating improved performance in visual adaptation tasks (VTAB-1k, FGVC), few-shot learning, domain adaptation, text classification (GLUE), and mathematical reasoning (GSM-8K). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding a way to make computers learn new skills without losing what they already know. This problem is called “fine-tuning” because it’s like fine-tuning an instrument to play a new song. The authors propose a new method, PACE, which helps computers keep learning new things while still remembering old ones. They tested their method on various tasks and found that it worked better than other methods in many cases. |
Keywords
» Artificial intelligence » Domain adaptation » Few shot » Fine tuning » Generalization » Parameter efficient » Regularization » Text classification
