Attention Mechanisms in Transformer Architectures
By Ibrahim Taofeek
A survey of attention mechanisms used in modern transformer models, from multi-head attention to sparse and linear variants.
Abstract
The transformer architecture has become the foundation of modern natural language processing and beyond. Central to its success is the attention mechanism.
Introduction
Attention allows models to dynamically focus on relevant parts of the input sequence. The original "Attention Is All You Need" paper introduced multi-head attention, which has since been extended in numerous directions.
Variants
- Multi-Head Attention: The original — parallel attention heads capture different relationships
- Sparse Attention: Reduces quadratic complexity by attending to a subset of positions
- Linear Attention: Reformulates the softmax to achieve linear complexity
- Flash Attention: IO-aware exact attention for efficient GPU training
Conclusion
Attention mechanisms continue to evolve. The trend is toward more efficient variants that maintain expressivity while reducing computational cost.
Citation
Taofeek, I. (2026). Attention Mechanisms in Transformer Architectures. ICML 2026.