Summary of Cast: Clustering Self-attention Using Surrogate Tokens For Efficient Transformers, by Adjorn Van Engelenhoven et al.
CAST: Clustering Self-Attention using Surrogate Tokens for Efficient Transformers
by Adjorn van Engelenhoven, Nicola Strisciuglio, Estefanía Talavera
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Transformer architecture has revolutionized the field of natural language processing by showing excellent performance across a wide range of tasks. However, its computational complexity, which increases quadratically with the length of input sequences, limits its application. To address this issue, we propose a novel Clustering self-Attention mechanism using Surrogate Tokens (CAST), which optimizes attention computation and achieves efficient Transformers. CAST utilizes learnable surrogate tokens to cluster input sequences and generate summaries, enabling information flow across the entire sequence. This reduces computational complexity from O(N^2) to O(alpha N), where alpha is constant according to the number of clusters and samples per cluster. Our experiments demonstrate that CAST performs similarly or better than baseline Transformers on long-range sequence modeling tasks while achieving higher efficiency in terms of time and memory usage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Transformer architecture has made a big impact in many areas, but it had some limitations. One problem was that the more information you wanted to process, the slower it got because it had to do a lot of complicated calculations. To fix this, scientists came up with a new idea called CAST (Clustering self-Attention mechanism using Surrogate Tokens). It helps speed things up by grouping similar bits of information together and then sharing that information between groups. This makes the whole process faster and more efficient. |
Keywords
* Artificial intelligence * Attention * Clustering * Natural language processing * Self attention * Transformer
