Summary of Softmax Attention with Constant Cost Per Token, by Franz A. Heinsen
Softmax Attention with Constant Cost per Token
by Franz A. Heinsen
First submitted to arxiv on: 8 Apr 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 A novel approach to transformers’ attention mechanism is presented, which replaces the traditional scaled dot-products with logarithms of scaled dot-products of exponentials. This modification linearizes attention by introducing exponential kernel feature maps, leading to infinite-dimensional feature functions. The proposed method can be expressed as a composition of log-sums of exponentials, allowing for constant-time and space complexity per token. Experimental results demonstrate the effectiveness of this alternative approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers is trying to improve how computers understand language by changing how they pay attention to different parts of what’s being said. Normally, computers use a special kind of math to figure out what’s important and what’s not, but these scientists are proposing a new way to do it that uses exponential functions. This new approach might help computers understand language better and faster. |
Keywords
* Artificial intelligence * Attention * Token
