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GLU Attention Improve Transformer

Created by
  • Haebom

Author

Zehao Wang

Outline

This paper proposes GLU Attention, a new attention mechanism that utilizes GLU (Gated Linear Units) to improve the performance of existing attention mechanisms. GLU Attention introduces nonlinearity to the attention value to improve model performance and convergence speed, and has minimal computational cost without additional parameters. It has been shown to be effective in text and vision modalities, and is also easy to integrate with other technologies such as Flash Attention, RoPE, and GQA. It has been released as open source on GitHub.

Takeaways, Limitations

Takeaways:
Improved performance and convergence speed of attention mechanisms without additional parameters.
Effective in both text and vision modalities.
High usability due to easy integration with other technologies.
Improving accessibility through open source disclosure.
Limitations:
Further validation of the generalizability of the presented experimental results is needed.
Additional experiments on different network structures and datasets are needed.
The performance improvement of GLU Attention may be limited to certain conditions.
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