Summary of Transformers Are Minimax Optimal Nonparametric In-context Learners, by Juno Kim et al.
Transformers are Minimax Optimal Nonparametric In-Context Learners
by Juno Kim, Tai Nakamaki, Taiji Suzuki
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 effectiveness of in-context learning (ICL) for large language models from a statistical learning theory perspective. The authors develop approximation and generalization error bounds for a transformer model, which is pre-trained on non-parametric regression tasks sampled from various function spaces. They show that sufficiently trained transformers can achieve or even improve upon the minimax optimal estimation risk in context by encoding relevant basis representations during pre-training. The analysis extends to high-dimensional or sequential data and distinguishes between pre-training and in-context generalization gaps. Additionally, the authors establish information-theoretic lower bounds for meta-learners regarding task diversity and representation learning for ICL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how large language models learn new tasks from just a few examples. The researchers use math to understand why this method works so well. They create rules for how well a model can perform when given some training data, and find that it’s possible to get even better results if you train the model on certain types of data before using it to learn new tasks. This helps us understand what makes in-context learning work, and could lead to even better performance in future models. |
Keywords
» Artificial intelligence » Generalization » Regression » Representation learning » Transformer
