Summary of Asymptotic Theory Of In-context Learning by Linear Attention, By Yue M. Lu et al.
Asymptotic theory of in-context learning by linear attention
by Yue M. Lu, Mary I. Letey, Jacob A. Zavatone-Veth, Anindita Maiti, Cengiz Pehlevan
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Disordered Systems and Neural Networks (cond-mat.dis-nn); 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 capabilities of Transformers in learning tasks without explicit prior training, a phenomenon known as in-context learning (ICL). The authors provide a precise answer to questions about sample complexity, pretraining task diversity, and context length for successful ICL by developing an exactly solvable model of linear regression using linear attention. The study derives sharp asymptotics for the learning curve in a scaling regime where token dimension increases with context length and pretraining task diversity. Experimental results show a double-descent learning curve and a phase transition between low and high task diversity regimes, demonstrating genuine ICL and generalization beyond pretrained tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper explores how Transformers can learn new tasks without prior training. The authors want to know what makes Transformers good at this, so they create a special model that can be solved exactly. They find that the key to success is having enough examples to train on, and that there’s a point where the Transformer starts memorizing tasks instead of learning them. This insight helps us understand how Transformers work and why they’re good at certain jobs. |
Keywords
» Artificial intelligence » Attention » Context length » Generalization » Linear regression » Pretraining » Token » Transformer
