The Transformer matters because it replaces recurrence with a parallel mechanism for mixing token information. That shift is what lets modern language models scale in both training efficiency and representational depth.
Why self-attention changed the baseline
Self-attention allows each token to compare itself with all other tokens in the current sequence, then produce a weighted summary. In practice, this means context is modeled through direct relationships rather than through a fixed sequential bottleneck.
- queries, keys, and values define how token relevance is computed
- multi-head attention lets different relational patterns emerge in parallel
- feed-forward layers turn contextualized token states into richer representations
- residual paths and normalization keep deep stacks trainable
Once these pieces are combined with large-scale pretraining, the architecture becomes a general engine for language understanding, generation, and downstream adaptation.