Summary of Benign Overfitting in Single-head Attention, by Roey Magen et al.
Benign Overfitting in Single-Head Attention
by Roey Magen, Shuning Shang, Zhiwei Xu, Spencer Frei, Wei Hu, Gal Vardi
First submitted to arxiv on: 10 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 This paper investigates the phenomenon of benign overfitting in single-head softmax attention models, which are fundamental building blocks of Transformers. The study proves that under specific conditions, these models can exhibit benign overfitting after just two steps of gradient descent, achieving near-optimal test performance despite fitting noisy training data. The research also explores how the signal-to-noise ratio (SNR) affects this behavior, showing that a large enough SNR is both necessary and sufficient for benign overfitting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Benign overfitting happens when a model fits noisy training data perfectly but still does well on new data. This paper looks at single-head softmax attention models, which are important parts of Transformers. They find that these models can get better and better as they learn, even though the training data is messy. The researchers also show how the quality of the training data affects this behavior. |
Keywords
» Artificial intelligence » Attention » Gradient descent » Overfitting » Softmax
