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Attention assigns importance weights to elements in an input sequence relative to other elements.
In machine learning, attention is a mechanism that assigns importance to different components of an input sequence relative to one another. In natural language processing, it uses “soft” weights over words in a sentence to indicate which words should influence the current representation or prediction more strongly. These weights are produced during the forward pass and can change at every step of processing. More generally, attention operates over token embeddings across a fixed-width sequence (from tens to millions of tokens). It enables each token to directly access information from any other token, rather than relying only on information carried through previous hidden states (as in many recurrent neural network designs). This capability helps models capture long-range and global dependencies more effectively, and it became central to transformer architectures, which replaced slower sequential processing with parallel attention. Attention weights are often interpreted as alignment scores in sequence-to-sequence tasks (e.g., translation), where the model learns which source tokens correspond to which target tokens. Because multiple-to-multiple alignments can be useful, soft attention (a weighted mixture of representations) is typically more effective than “hard” attention that selects a single token. The concept also extends beyond NLP to vision and other modalities, and attention maps are commonly visualized to inspect what different model heads focus on.
Attention assigns importance weights to elements in an input sequence relative to other elements.
Soft attention weights are computed in the forward pass and change with each input step, enabling weighted mixtures of token representations.
Attention supports direct token-to-token information access, helping capture global dependencies and improving over purely sequential recurrent information flow.
In tasks like translation, attention weights can be interpreted as alignment between source and target tokens, often in a nuanced many-to-many way.
A mechanism that computes importance weights for elements in an input sequence so the model can focus on the most relevant parts when forming representations or predictions.
Attention that uses continuous (non-binary) weights over tokens, producing a weighted sum of representations rather than selecting a single token.
Attention that selects one token (weight 1) while setting others to 0, typically involving different training behavior than soft attention.
The learned correspondence between tokens in a source sequence and tokens in a target sequence, often reflected by attention weight patterns.
A vector representation of a token used as the input to attention mechanisms across a sequence.
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