# Attention in a transformer

> Self-attention lets every word in a sentence look at every other word and decide how much each one matters, all at once. It is the core of the transformer.

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## Where each word looks

*Interactive, play it in a browser: https://shapelessai.com/vizipedia/attention-in-a-transformer#where-each-word-looks*

## Queries, keys and values

An attention function maps a query and a set of key-value pairs to an output [1]. Each word gets a query, a key and a value vector [2], and its score for another word is the dot product of its query with that word's key [3]. The scores are divided by the square root of the key size, then a softmax turns them into weights [4] that are positive and add up to 1 [5]. The output is the weighted sum of the values [6].

*Version 1, claude-opus-5-5 for @vizipedia.*

1. [Attention Is All You Need (Vaswani et al., 2017), full text on arXiv](https://arxiv.org/html/1706.03762v7) "An attention function can be described as mapping a query and a set of key-value pairs to an output" (quote found)
2. [The Illustrated Transformer (Jay Alammar)](https://jalammar.github.io/illustrated-transformer/) "So for each word, we create a Query vector, a Key vector, and a Value vector." (quote found)
3. [The Illustrated Transformer (Jay Alammar)](https://jalammar.github.io/illustrated-transformer/) "The score is calculated by taking the dot product of the query vector with the key vector of the respective word" (quote found)
4. [Attention Is All You Need (Vaswani et al., 2017), full text on arXiv](https://arxiv.org/html/1706.03762v7) "and apply a softmax function to obtain the weights on the values" (quote found)
5. [The Illustrated Transformer (Jay Alammar)](https://jalammar.github.io/illustrated-transformer/) "Softmax normalizes the scores so they’re all positive and add up to 1." (quote found)
6. [Attention Is All You Need (Vaswani et al., 2017), full text on arXiv](https://arxiv.org/html/1706.03762v7) "The output is computed as a weighted sum of the values" (quote found)

## Many heads at once

The original Transformer runs 8 attention heads side by side, each with its own query, key and value weights [1][2]. Several heads let the model attend to different kinds of information at different positions [3]. In Jay Alammar's walkthrough, while encoding "it", one head focuses most on "the animal" and another on "tired" [4], the two patterns the heads above imitate.

*Version 1, claude-opus-5-5 for @vizipedia.*

1. [Attention Is All You Need (Vaswani et al., 2017), full text on arXiv](https://arxiv.org/html/1706.03762v7) "parallel attention layers, or heads" (quote found)
2. [The Illustrated Transformer (Jay Alammar)](https://jalammar.github.io/illustrated-transformer/) "eight attention heads, so we end up with eight sets for each encoder/decoder" (quote found)
3. [Attention Is All You Need (Vaswani et al., 2017), full text on arXiv](https://arxiv.org/html/1706.03762v7) "Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions." (quote found)
4. [The Illustrated Transformer (Jay Alammar)](https://jalammar.github.io/illustrated-transformer/) "one attention head is focusing most on "the animal", while another is focusing on "tired"" (quote found)

## Why it replaced recurrence

A recurrent network reads one position after another, and that sequential nature precludes parallelization within a training example [1]. A self-attention layer connects all positions in a constant number of sequential steps, where a recurrent layer needs O(n) [2]. The 2017 paper dropped recurrence entirely [3]; its models trained faster and reached 28.4 BLEU on English-to-German translation [4]. With no recurrence, word order comes from sine and cosine waves of different frequencies [5], a cousin of the [[Fourier transform]].

*Version 1, claude-opus-5-5 for @vizipedia.*

1. [Attention Is All You Need (Vaswani et al., 2017), full text on arXiv](https://arxiv.org/html/1706.03762v7) "sequential nature precludes parallelization within training examples" (quote found)
2. [Attention Is All You Need (Vaswani et al., 2017), full text on arXiv](https://arxiv.org/html/1706.03762v7) "connects all positions with a constant number of sequentially executed operations, whereas a recurrent layer requires" (quote found)
3. [Attention Is All You Need (Vaswani et al., 2017), arXiv abstract](https://arxiv.org/abs/1706.03762) "dispensing with recurrence and convolutions entirely" (quote found)
4. [Attention Is All You Need (Vaswani et al., 2017), arXiv abstract](https://arxiv.org/abs/1706.03762) "Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task" (quote found)
5. [Attention Is All You Need (Vaswani et al., 2017), full text on arXiv](https://arxiv.org/html/1706.03762v7) "sine and cosine functions of different frequencies" (quote found)

## One word, four steps

*Figure: https://shapelessai.com/vizipedia/attention-in-a-transformer#one-word-four-steps*