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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Self-attention lets a Transformer update each token’s representation using information from other positions in the same sequence. It does this by comparing learned query and key vectors, turning those comparisons into weights, and using the weights to mix value vectors. The operation is central to Transformers, but it is only one part of the architecture.
How self-attention works
Think of each position as determining which other positions may be useful for updating its representation. This is an analogy, not a claim that a model consciously focuses: the computation uses learned vectors and normalized scores.
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Given input representations X, learned linear projections produce queries Q, keys K, and values V. The scaled dot-product attention operation is:
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Attention(Q, K, V) = softmax(QKT / √dk)V
Here, a query is compared with keys to score how relevant each position is; dividing by the square root of the key dimension dk scales those scores. Softmax turns the scores into weights, and those weights are applied to the values to produce a weighted mixture. In self-attention, queries, keys, and values are projected from the same sequence representation. A mask may restrict which positions can contribute.
Why Transformers use multiple heads and positional information
Multiple heads
Multi-head attention uses separate learned projections to calculate several attention operations in parallel. Their outputs are concatenated and projected to form the layer output. Multiple heads provide distinct learned projections, but that does not mean a particular head always corresponds to a fixed linguistic concept.
Position matters
Self-attention alone does not encode token order: by itself, it does not know whether a token came first or later in the sequence. The original Transformer adds positional encodings to its input embeddings to provide that information.
Rank #2
Attention is one component of a block
The original Transformer also uses position-wise feed-forward networks, residual connections, and layer normalization. These components help transform information across layers; self-attention is not the whole architecture.
Self-attention and cross-attention are different
| Operation | Queries come from | Keys and values come from | What it connects |
|---|---|---|---|
| Self-attention | The sequence being updated | The same sequence representation | Positions within one sequence |
| Cross-attention | The decoder representation | Encoder outputs | Decoder positions to encoded input information |
In encoder self-attention, positions can draw on other positions in the input sequence. Decoder self-attention is different when generating outputs: a causal mask blocks access to subsequent output positions, so a prediction cannot use future target tokens. In encoder-decoder cross-attention, decoder queries are compared with encoder keys and values; because the two sides use different representations, this is not self-attention.
Rank #3
Why full self-attention can be expensive
Full self-attention computes interactions across sequence positions. Its attention-score matrix therefore grows quadratically with sequence length: doubling the length means four times as many pairwise position interactions. This cost can make memory and computation substantial for long sequences. In exchange, positions can interact directly, and the operation allows parallel computation across positions during training.
Linear-attention alternatives
Alternative formulations change how attention is computed, and their trade-offs depend on the method and task. Katharopoulos and coauthors describe a linear-attention formulation using kernel feature maps and matrix associativity to reduce sequence-length complexity from O(N²) to O(N). In their 2020 experiments, they reported up to 4000× speed for autoregressive prediction of very long sequences. That is a result for their experiments, not a general speed guarantee for every model, task, or workload. Read the paper on linear attention.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What attention weights can—and cannot—tell you
The equation explains how a layer combines value vectors; it does not establish that attention weights provide a complete explanation of a model’s reasoning. They show the weights used in that operation, not a full account of why the model produced an answer.
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What the original Transformer paper reported
Vaswani and coauthors introduced the Transformer architecture in 2017. Their paper reports 28.4 BLEU on the WMT 2014 English-to-German task and 41.8 BLEU on WMT 2014 English-to-French. These are reported translation results, not measures of attention quality. The Google Research record displays 41.0 for English-to-French, while the original arXiv paper abstract states 41.8; the figures should not be merged. See the original paper and Google Research’s record.
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