Attention Matrix

Attention matrices, central to transformer-based models, represent the relationships between input elements (e.g., words in a sentence, pixels in an image), guiding information flow and shaping model outputs. Current research focuses on improving the efficiency of attention computation, particularly for long sequences, through techniques like sparse attention, low-rank approximations, and novel attention mechanisms (e.g., linear attention, convolution-based attention). These advancements aim to reduce the quadratic complexity of standard attention, enabling the application of transformer models to larger datasets and more complex tasks, with significant implications for natural language processing, computer vision, and other fields.