Self-Attention

ELI5 — The Vibe Check

Self-attention is how a model looks at a sentence and figures out which words are most important to each other. When you read 'The cat sat on the mat because it was tired,' self-attention is what helps the model know that 'it' refers to 'the cat,' not 'the mat.' Every word gets to 'look at' every other word and decide how much to care about it.

Real Talk

Self-attention is a mechanism where each element in a sequence computes attention weights over all other elements in the same sequence. Each position produces query, key, and value vectors — attention scores are computed as scaled dot products of queries and keys, then used to weight values. It's the core operation of transformer architectures, enabling parallel processing of sequence data.

When You'll Hear This

"Self-attention captures long-range dependencies better than RNNs." / "The self-attention maps show the model is focusing on the right tokens."