Summary of Benign Overfitting in Token Selection Of Attention Mechanism, by Keitaro Sakamoto and Issei Sato
Benign Overfitting in Token Selection of Attention Mechanism
by Keitaro Sakamoto, Issei Sato
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 attention mechanism in transformer models is crucial for their success, but the underlying theoretical understanding is still developing. This paper investigates the training dynamics and generalization capabilities of attention under classification problems with noisy labels. The results show that attention achieves benign overfitting, retaining good generalization performance despite fitting noise. A delayed acquisition of generalization after initial overfitting is also observed. These findings are supported by experiments on synthetic and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Attention in transformer models helps them succeed, but we don’t fully understand how it works. This paper looks at how attention behaves when classifying with noisy labels. Attention does something surprising – it gets really good at picking the right tokens despite fitting the noise. It takes a little while for this to happen, but then it stays that way. The results were tested on fake and real data. |
Keywords
* Artificial intelligence * Attention * Classification * Generalization * Overfitting * Transformer
