Rethinking Residual Connections in Transformer Architectures
The article discusses a new approach to residual connections in transformer models, called Attention-Residuals, which aims to address the representation collapse issue in deep transformer models.
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Why it matters
Rethinking the residual connections in transformer architectures could lead to significant improvements in the performance and robustness of deep learning models.
Key Points
- 1The standard transformer block uses a simple additive residual connection, which can lead to the residual stream dominating the attention signal as models get deeper.
- 2Attention-Residuals proposes a different wiring where the residual pathway and attention computation are more tightly coupled.
- 3The article compares the standard approach to the Attention-Residuals approach and discusses the potential benefits of the new approach.
Details
Transformer models have become ubiquitous in deep learning, with the standard transformer block architecture remaining largely unchanged since the original
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