Attention in transformers, step-by-step | Deep Learning Chapter 6

Transformer architects and natural language processing researchers use attention mechanisms to solve the fundamental problem that word embeddings initially assign the same vector to a word regardless of context.

Transformer architects implement attention through three learned weight matrices that transform each word’s embedding into query, key, and value vectors, analogous to database operations where queries search through keys to retrieve values.

Each word’s query vector computes similarity scores with every word’s key vector, creating an attention pattern matrix that reveals which words should influence each other’s representations.

Softmax transforms raw attention scores into normalized weights that sum to one, creating interpretable probability distributions over which words receive attention.

After attention weights are computed, value vectors get combined through weighted sums, aggregating contextual information from relevant words into each word’s updated representation.

Transformers use multiple attention heads running in parallel, each with separate query-key-value matrices that learn to capture different types of relationships between words.

Attention mechanisms transform static word embeddings into context-dependent representations where the same word receives different vector representations depending on surrounding context.

Transformers require explicit positional encoding because attention operations are permutation-invariant: without position information, “dog bites man” and “man bites dog” would produce identical representations.