| Challenge: | Recent studies have suggested that sparse attention mechanisms can be made more interpretable by replacing the softmax activation with its sparser variants. |
| Approach: | They propose a method to replace softmax activation with a ReLU to achieve sparsity in attention by layer normalization with either a specialized initialization or an additional gating function. |
| Outcome: | The proposed model is easy to implement and more efficient than previously proposed sparse attention mechanisms. |
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Yutao Sun, Tianzhu Ye, Li Dong, Yuqing Xia, Jian Chen, Yizhao Gao, Shijie Cao, Jianyong Wang, Furu Wei
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| Challenge: | Existing approaches to sparsify attention in the Transformer model are based on quadratic memory complexity and a lack of information for each word. |
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Hard-Coded Gaussian Attention for Neural Machine Translation (2020.acl-main)
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The Sparse Frontier: Sparse Attention Trade-offs in Transformer LLMs (2026.findings-acl)
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| Challenge: | Sparse attention is a promising strategy to extend long-context capabilities in LLMs . but its efficiency–accuracy trade-offs remain unclear due to the lack of comprehensive evaluation . |
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