Summary of More Expressive Attention with Negative Weights, by Ang Lv et al.
More Expressive Attention with Negative Weights
by Ang Lv, Ruobing Xie, Shuaipeng Li, Jiayi Liao, Xingwu Sun, Zhanhui Kang, Di Wang, Rui Yan
First submitted to arxiv on: 11 Nov 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 proposed novel attention mechanism, Cog Attention, enables negative attention weights for enhanced expressiveness in Transformer-like models. This stems from two key factors: parameter flexibility and robustness against representational collapse. Cog Attention naturally learns to perform multiple operations simultaneously within a single head, allowing the OV matrix to focus on refinement or modification. The approach also prevents over-squashing of earlier tokens into later positions. Experiments show that models using Cog Attention exhibit superior performance compared to those employing traditional softmax attention modules. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cog Attention is a new way for computers to pay attention while learning. It lets the computer do more complex tasks by allowing negative numbers in its “attention weights”. This helps it learn and remember things better. The approach also prevents the computer from getting stuck or repeating itself, which can make it more accurate. Cog Attention works well with different types of data and tasks, such as language modeling and image generation. |
Keywords
» Artificial intelligence » Attention » Image generation » Softmax » Transformer
