Summary of On the Role Of Attention Masks and Layernorm in Transformers, by Xinyi Wu et al.
On the Role of Attention Masks and LayerNorm in Transformers
by Xinyi Wu, Amir Ajorlou, Yifei Wang, Stefanie Jegelka, Ali Jadbabaie
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 In this paper, researchers investigate the limitations of transformers, specifically the pure self-attention mechanism, which is crucial for foundation models. They find that as depth increases, self-attention suffers from rank collapse, reducing model expressivity and utilization. The authors analyze rank collapse under self-attention, considering attention masks and layer normalization. While masked attention still collapses to a rank one subspace, local masked attention slows down the rate of collapse. With layer normalization, they show that certain value matrices lead to exponential collapse, but others result in diverse equilibria with any possible rank. This work refutes previous assumptions about layer normalization’s role and suggests self-attention with layer normalization is more expressive than thought. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformers are super important for building big AI models. The problem is that they can get stuck as they get deeper, which limits how well they can do certain tasks. Researchers looked at what causes this “rank collapse” and found that it’s related to something called self-attention. They also discovered that adding another thing called layer normalization helps. This means that transformers might be more powerful than we thought! |
Keywords
» Artificial intelligence » Attention » Self attention
