Summary of Trained Transformer Classifiers Generalize and Exhibit Benign Overfitting In-context, by Spencer Frei and Gal Vardi
Trained Transformer Classifiers Generalize and Exhibit Benign Overfitting In-Context
by Spencer Frei, Gal Vardi
First submitted to arxiv on: 2 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 The paper investigates the behavior of linear transformers trained on random linear classification tasks. By analyzing the implicit regularization of gradient descent, it characterizes how many pre-training tasks and in-context examples are needed for the trained transformer to generalize well at test-time. The results show that these trained transformers can exhibit “benign overfitting in-context,” where they memorize noisy labels but still generalize near-optimally for clean test examples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how linear transformers, a type of AI model, learn when given random tasks and examples to practice with. It finds out that these models need a certain number of practice tasks and examples to do well on new, unseen problems. Interestingly, the model can get too good at memorizing noisy information but still make accurate predictions for clean data. |
Keywords
» Artificial intelligence » Classification » Gradient descent » Overfitting » Regularization » Transformer
