Grammatically-Guided Sparse Attention for Efficient and Interpretable Transformers

ArXi:2605.24518v1 Announce Type: cross 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