Attention Mechanism

Multi-Head Attention and Positional Encoding: Inside the Transformer

Multi-head attention runs several attention operations in parallel, letting the model simultaneously capture syntactic structure, semantic relationships, and coreference. Positional encoding solves a key problem: since attention is order-agnostic, position information must be explicitly injected. These two mechanisms together define transformer expressiveness.

What is the Attention Mechanism in Deep Learning?

The attention mechanism lets neural networks focus on the most relevant parts of their input when producing each output — similar to how you re-read a specific paragraph of a contract before answering a question about it. It's the core innovation that made transformers, and therefore modern LLMs, possible.