Grammatically-Guided Sparse Attention for Efficient and Interpretable Transformers

arXiv:2605.24518v1 Announce Type: new Abstract: The quadratic complexity of self-attention in Transformer models remains a significant bottleneck for processing long sequences and deploying large language models efficiently. For this approach, there has been significant research into Sparse Attention, and Deepseek Sparse Attention has combined various methods of creating segments of tokens to reduce the time complexity. This paper introduces a novel approach, Grammatically-Guided Sparse Attention, which constrains attention computations based on the grammatical roles of tokens. By leveraging P

Source: arXiv cs.CL — read the full report at the original publisher.

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