Summary of Stick-breaking Attention, by Shawn Tan et al.
Stick-breaking Attention
by Shawn Tan, Yikang Shen, Songlin Yang, Aaron Courville, Rameswar Panda
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper proposes an alternative self-attention mechanism based on the stick-breaking process, which naturally incorporates recency bias motivated by linguistic grammar parsing. This mechanism replaces the traditional softmax operator and allows for a sequence of attention weights. The authors study the implications of this new mechanism and find that it performs competitively with current methods on length generalisation and downstream tasks. They also implement numerically stable stick-breaking attention and adapt Flash Attention to accommodate this mechanism. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about creating a new way for computers to understand language, called self-attention. Right now, this process relies on special tokens that help the computer understand the order of words. But this doesn’t always work well when dealing with longer texts or different languages. The researchers propose a new method based on how we break sticks into smaller pieces. This helps the computer learn more about the context and relationships between words. They tested this new method and found it works as well as other methods, even improving performance in some cases. |
Keywords
» Artificial intelligence » Attention » Parsing » Self attention » Softmax
