Self-Attention: Every Token Looks at Every Token
Take Bahdanau's machinery and point the queries at the very same sequence being read — and suddenly one layer connects any two positions directly. Here is the attention matrix, the causal mask, the O(T²) bill, and why this single primitive now runs the world's models.
01.The Problem: Waiting in Line to Understand One Sentence
Read this sentence out loud:
"The animal didn't cross the street because it was too tired."
What does "it" refer to?
You answered instantly: the animal, not the street.
You did that by connecting word 8 to word 2 — across the whole sentence, in one hop.
Now remember how RNNs read (Topic 112, in one sentence: an RNN consumes tokens one by one, carrying a running summary in its hidden state).
By the time an RNN reaches "it", everything about "the animal" has been compressed into a running state that has also passed through every word in between.
Bahdanau attention (Topic 118) improved the handoff: the decoder could look back at all encoder states.
But both sides were still separate machines. The 2017 question was more radical:
What if the queries come from the very sequence being read?
Then a sentence could look at itself — every word consulting every other word — no encoder, no decoder, no relay line.
That is self-attention: attention where the asker and the answer-book are the same sequence.
Self-Attention: The Sequence Reads Itself 🔭
Self-Attention: The Sequence Reads Itself 🔭
Queries, keys, and values all come from the same tensor X, through three different learned projections. Result: one matrix multiply replaces the entire unrolled RNN chain — every pair of positions becomes adjacent.
Unlock Topic #119: Self-Attention: Every Token Looks at Every Token
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