Transformer Performance

Transformer model performance is a central research area aiming to understand and improve the efficiency and accuracy of these powerful neural networks across diverse applications. Current research focuses on optimizing hyperparameters, exploring alternative attention mechanisms (e.g., modifying the key-query interaction), and developing more efficient architectures (e.g., reducing parameter counts through pruning or using simpler components like MLPs). These efforts are crucial for expanding the applicability of Transformers to resource-constrained environments and for gaining deeper insights into their internal workings, ultimately leading to more robust and interpretable AI systems.