Summary of Generalized Probabilistic Attention Mechanism in Transformers, by Dongnyeong Heo and Heeyoul Choi
Generalized Probabilistic Attention Mechanism in Transformers
by DongNyeong Heo, Heeyoul Choi
First submitted to arxiv on: 21 Oct 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 The paper presents a theoretical analysis on addressing two well-known issues in Transformer architecture’s attention mechanism: rank-collapse and gradient vanishing. The authors propose a novel class of attention mechanisms, Generalized Probabilistic Attention Mechanism (GPAM), which allows for negative attention scores while preserving a fixed total sum. They introduce the dual-attention implementation of GPAM within the Transformer architecture, dubbed daGPAM. Theoretical evidence shows that daGPAM effectively mitigates both issues, and empirical validation demonstrates its superiority over alternative attention mechanisms. Furthermore, the authors demonstrate practical benefits of GPAM in natural language processing tasks such as language modeling and neural machine translation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a big problem in computer models called Transformers. These models are really good at understanding human language, but they have some issues that make them not work well sometimes. The researchers found out why these issues happen and created a new way to fix them. They tested this new way on some language tasks and it worked better than other ways people tried to solve the same problems. |
Keywords
» Artificial intelligence » Attention » Natural language processing » Transformer » Translation
