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The technique that lets a model weigh how much each part of the input matters when producing each part of the output — the core operation inside every Transformer.
Attention was introduced for machine translation by Bahdanau et al. (2015), letting a decoder look back at the most relevant source words instead of a single fixed summary vector. Vaswani et al. (2017) then made self-attention the whole architecture, removing recurrence entirely.
Each token computes a weighted combination of all others, where the weights say how relevant each is. This is how a model resolves references, tracks long-range structure and decides what to focus on.
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