Summary of Non-asymptotic Convergence Of Training Transformers For Next-token Prediction, by Ruiquan Huang et al.
Non-asymptotic Convergence of Training Transformers for Next-token Prediction
by Ruiquan Huang, Yingbin Liang, Jing Yang
First submitted to arxiv on: 25 Sep 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 provides a fine-grained non-asymptotic analysis of the training dynamics of a one-layer transformer for next-token prediction (NTP) tasks. The authors develop a mathematical framework based on partial orders to characterize the essential structural properties of training datasets for NTP. A two-stage training algorithm is designed, which exhibits fast convergence performance in both pre-processing and main stages. Specifically, the feed-forward layer converges sub-linearly to its max-margin solution, while the attention layer also converges sub-linearly to its corresponding max-margin solution. The cross-entropy loss enjoys a linear convergence rate. The trained transformer demonstrates non-trivial prediction ability with dataset shift, shedding light on the remarkable generalization performance of transformers. Novel properties are developed on the attention gradient, contributing to the convergence of the training process. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how transformers work better in certain tasks. It looks at a type of transformer called one-layer and analyzes its training process. The authors come up with a new way to think about datasets for this task, using something called partial orders. They then design a special way of training the model that makes it learn quickly. This is important because transformers are very good at making predictions, even when the data they’re seeing is different from what they learned on. The paper also finds that some parts of the transformer learn faster than others and shows how this affects its ability to make predictions. |
Keywords
» Artificial intelligence » Attention » Cross entropy » Generalization » Token » Transformer
