The Transformer architecture revolutionized NLP by replacing recurrence with self-attention, enabling unprecedented parallelization and performance.
This groundbreaking architecture powers modern language models like BERT, GPT, and T5.
Parallel attention mechanisms that capture different relationships
Position-wise fully connected layers for non-linear transformations
Inject sequence order information without recurrence
Stabilize training and improve convergence