Summary of Breaking the Attention Bottleneck, by Kalle Hilsenbek
Breaking the Attention Bottleneck
by Kalle Hilsenbek
First submitted to arxiv on: 16 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 The paper proposes a novel attention mechanism replacement in transformer architectures, addressing the quadratic complexity bottleneck that limits their adoption. By developing a generative function to replace traditional attention mechanisms, the authors achieve smaller models with lower loss and improved performance on nanoGPT test sets. The approach is auto-regressive, incorporating previous tokens for prediction. Additionally, an average context vector is introduced to further reduce loss. This attention replacement concept is open-sourced under the GNU AGPL v3 license. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper improves transformer architecture by replacing traditional attention mechanisms with a new generative function. This makes models smaller and more efficient without losing accuracy. The approach still predicts each token based on previous ones, like usual transformers. To get even better results, an average context vector is added. You can try this idea yourself and see how it works! |
Keywords
» Artificial intelligence » Attention » Token » Transformer
