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How does Transformer self-attention mechanism ($Q, K, V$) compute contextual token representations?

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Edited by Aravind Patel · Aug 24, 2026 9:25 AM

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Title snapshot

How does Transformer self-attention mechanism ($Q, K, V$) compute contextual token representations?

Summary snapshot
Dissecting Scaled Dot-Product Attention: $Attention(Q, K, V) = softmax(\frac{QK^T}{\sqrt{d_k}})V$.
Content snapshot
### Attention Formula Query ($Q$), Key ($K$), and Value ($V$) matrices transform input token vectors. Dot products of $Q$ and $K$ calculate dynamic alignment weights across all sequence tokens simultaneously.
Source snapshot

https://arxiv.org/abs/1706.03762

Version 1 (Original Post)

Published by Aravind Patel · Aug 9, 2026 5:37 AM

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Original Title

How does Transformer self-attention mechanism ($Q, K, V$) compute contextual token representations?

Original Summary
Dissecting Scaled Dot-Product Attention: $Attention(Q, K, V) = softmax(\frac{QK^T}{\sqrt{d_k}})V$.
Original Content
### Attention Formula Query ($Q$), Key ($K$), and Value ($V$) matrices transform input token vectors. Dot products of $Q$ and $K$ calculate dynamic alignment weights across all sequence tokens simultaneously.
Original Sources

https://arxiv.org/abs/1706.03762