Attention Weight Matrix

Attention weight matrices are crucial components of transformer-based models, revealing how these models process information by weighting the relationships between different elements (e.g., words in a sentence, pixels in an image, nodes in a graph). Current research focuses on analyzing these matrices to understand model behavior, improve model robustness (e.g., against adversarial attacks), and optimize model efficiency through techniques like sparse attention. Understanding and manipulating attention weight matrices is vital for enhancing the interpretability, performance, and resource efficiency of various deep learning models across diverse applications, including natural language processing, computer vision, and graph neural networks.