longformer attention

**Longformer attention** is the **sparse attention mechanism combining sliding-window local attention with selected global attention tokens for long-sequence processing** - it enables substantially longer contexts than dense transformer attention at lower cost. **What Is Longformer attention?** - **Definition**: Attention pattern where each token attends locally while special tokens receive global visibility. - **Complexity Profile**: Reduces compute growth compared with full quadratic attention. - **Global Token Role**: Key positions such as query or separator tokens aggregate document-wide information. - **Use Cases**: Long-document classification, QA, and retrieval-intensive language tasks. **Why Longformer attention Matters** - **Scalability**: Supports long inputs that are impractical with standard dense attention. - **Performance Balance**: Preserves local context detail while retaining targeted global reasoning. - **RAG Fit**: Helpful for processing large packed evidence sets in a single pass. - **Infrastructure Relief**: Lower memory pressure improves deployment feasibility. - **Design Tradeoff**: Global token placement and window size strongly affect quality. **How It Is Used in Practice** - **Window Tuning**: Select local attention span based on task dependency length. - **Global Token Strategy**: Assign global attention to instruction, question, or anchor tokens. - **Evaluation**: Benchmark against dense baselines for accuracy, latency, and memory footprint. Longformer attention is **a widely used sparse-attention design for long documents** - Longformer patterns provide practical long-context gains with manageable compute costs.