Attention Mechanism

Attention mechanisms let models weigh the importance of different input elements dynamically. Self-attention, the core of transformers, allows each position in a sequence to attend to all other positions. This enables models to capture long-range dependencies and contextual relationships, which was a major limitation of previous architectures like RNNs.