Attention Mechanism

The attention mechanism allows models to dynamically weigh the importance of different input elements. Instead of processing all inputs equally, attention learns to “pay attention” to what matters most.

Self-Attention: The key innovation in transformers. Each token in a sequence attends to every other token, enabling the model to capture relationships regardless of distance. This solved the long-range dependency problem that plagued RNNs and LSTMs.

Multi-Head Attention: Running multiple attention operations in parallel, each learning different relationship types (syntactic, semantic, positional). This allows the model to capture diverse patterns simultaneously.

The 2017 paper “Attention Is All You Need” introduced the transformer architecture built entirely on attention, eliminating recurrence and convolution. This breakthrough enabled the scaling of language models to billions of parameters and powers all modern LLMs.