Summary of Transformers Get Stable: An End-to-end Signal Propagation Theory For Language Models, by Akhil Kedia et al.
Transformers Get Stable: An End-to-End Signal Propagation Theory for Language Models
by Akhil Kedia, Mohd Abbas Zaidi, Sushil Khyalia, Jungho Jung, Harshith Goka, Haejun Lee
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); 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 addresses the challenge of scaling transformer models in depth, a crucial aspect for their continued success. The authors develop a unified signal propagation theory, providing mathematical formulae to understand and mitigate issues like vanishing/exploding gradients, rank collapse, and instability. They propose DeepScaleLM, an initialization scheme that preserves unit output/gradient moments throughout the model, enabling the training of extremely deep models with 1000 layers. The results show that transformer models can be much deeper; deep models with fewer parameters outperform shallow ones in various tasks like Language Modeling, Speech Translation, and Image Classification. These improvements also translate to better performance on downstream Question Answering tasks and improved robustness for Image Classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make powerful computer models called transformers even more effective. The authors create a new way to understand how these models work and fix problems that make them less accurate when they’re very deep. They also develop a special technique to train these models, which lets them be much deeper than before. This means the models can learn even better from large amounts of data. The results show that these improved models perform better in various tasks like language translation, image recognition, and question answering. |
Keywords
* Artificial intelligence * Image classification * Question answering * Transformer * Translation
