Summary of Model Balancing Helps Low-data Training and Fine-tuning, by Zihang Liu et al.
Model Balancing Helps Low-data Training and Fine-tuning
by Zihang Liu, Yuanzhe Hu, Tianyu Pang, Yefan Zhou, Pu Ren, Yaoqing Yang
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 A novel approach to fine-tuning pre-trained models for specialized domains is proposed in this paper, leveraging insights from Heavy-Tailed Self-Regularization (HT-SR) theory. By analyzing the shape of empirical spectral densities (ESDs), researchers reveal an imbalance in training quality across different model layers. To address this issue, they adapt a recently proposed layer-wise learning rate scheduler, TempBalance, which effectively balances training quality and enhances low-data training and fine-tuning for both NLP and SciML tasks. The results demonstrate increasing performance gains as the amount of available tuning data decreases, making TempBalance an effective “add-on” method for improving model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how we can train models to work better with small amounts of data. Currently, we use pre-trained models and add a little bit of new information to make them more accurate in specific areas like language or science. But sometimes these models don’t perform well when they have very little new data. The researchers discovered that this is because some parts of the model are trained better than others. They created a new way, called TempBalance, to help balance the training and make the model work better with less data. This method can be used for both language and scientific tasks. |
Keywords
» Artificial intelligence » Fine tuning » Nlp » Regularization
