Summary of Selective Attention Improves Transformer, by Yaniv Leviathan et al.
Selective Attention Improves Transformer
by Yaniv Leviathan, Matan Kalman, Yossi Matias
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper introduces Selective Attention, a modification to the standard attention mechanism that reduces attention to unneeded elements. This simple parameter-free change improves language modeling performance across various model sizes and context lengths. For instance, transformers trained on C4 with selective attention perform similarly to those with more heads and parameters in their attention modules. Additionally, selective attention allows decreasing the size of the attention’s context buffer, resulting in meaningful reductions in memory and compute requirements during inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper makes a simple change to the standard attention mechanism that helps language models work better. This change is called Selective Attention, and it reduces how much the model focuses on things it doesn’t need. As a result, language modeling performance improves, regardless of the size or type of model used. For example, some transformers were trained on C4 with this new attention method and performed just as well as others that used more resources. |
Keywords
» Artificial intelligence » Attention » Inference
