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What are the latest advancements in transformer architectures?
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Recent advancements in transformer architectures have primarily focused on improving efficiency, scaling, and extending their capabilities beyond natural language. Here are some key developments:
Efficiency Improvements - **Sparse Attention**: Models like the Longformer [Child et al., 2019] use sparse attention patterns to handle longer sequences with lower computational cost. - **Linear Attention**: Variants such as the Performer [Choromanski et al., 2020] approximate the attention mechanism, reducing the complexity from O(n²) to O(n).
Scaling and Architecture - **Mixture of Experts (MoE)**: Architectures like GLaM [Du et al., 2021] and the Switch Transformer [Fedus et al., 2021] use sparsely activated expert layers to scale up model size dramatically without a proportional increase in computational cost per token. - **Vision Transformers (ViT)**: The Vision Transformer [Dosovitskiy et al., 2020] adapted the transformer architecture for image recognition tasks by treating image patches as tokens, demonstrating its versatility.
Key Papers | Paper | Contribution | |---|---| | Attention Is All You Need | Introduced the original Transformer model. | | ViT | Applied Transformers to computer vision. | | Switch Transformer | Introduced massive-scale MoE models. |
These advancements have enabled transformers to become the foundation for state-of-the-art models across a wide range of domains.
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