Multi Head Attention

Multi-head attention is a mechanism within transformer networks that allows the model to attend to different aspects of input data simultaneously, improving performance on various tasks. Current research focuses on optimizing multi-head attention for efficiency, including exploring alternative architectures like grouped-query attention and methods to reduce computational complexity without sacrificing accuracy, such as pruning or low-precision approximations. These advancements are significant because they enable the application of transformer models to larger datasets and more complex problems across diverse fields, including image processing, audio classification, and natural language processing.