Summary of On the Power Of Convolution Augmented Transformer, by Mingchen Li et al.
On the Power of Convolution Augmented Transformer
by Mingchen Li, Xuechen Zhang, Yixiao Huang, Samet Oymak
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Neural and Evolutionary Computing (cs.NE)
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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 research paper proposes a new architecture called Convolution-Augmented Transformer (CAT) for natural language processing tasks. By incorporating convolutional filters into the attention layer, CAT leverages both local contextual information and global semantic understanding. The authors demonstrate that CAT can solve complex tasks like associative recall, copying, and length generalization with improved performance compared to existing models like Mamba or transformer. Additionally, they highlight the computational benefits of combining convolution and attention, which can reduce the need for full attention by summarizing context windows and creating salient summary tokens. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CAT is a new approach that combines the strengths of transformers and convolutional neural networks (CNNs). By using convolutional filters in the attention layer, CAT can learn local patterns and relationships in language data. The authors show that this approach leads to improved performance on tasks like associative recall, copying, and length generalization. |
Keywords
» Artificial intelligence » Attention » Generalization » Natural language processing » Recall » Transformer
