There is something uncanny about attention mechanisms.
Not just as a technical matter — though the mathematics is elegant — but as a philosophical one. The transformer architecture, at its core, is a model of relevance. For each token in a sequence, it asks: what else here matters? What should I be looking at?
This is, in a surprising way, how we move through the world.
The Query, the Key, the Value
In the original formulation, attention is computed by comparing queries against keys to produce weights, which then modulate values. The language is almost too suggestive to be coincidental. A query is an expression of desire — what am I looking for? A key is an identity — what am I? A value is the actual content — what do I carry?
We do this constantly. In conversation, in reading, in memory. Some phrase from three years ago rises unbidden because something present has called to it.
On Learning Language by Learning Context
What struck me about large language models was not the fluency, which arrived gradually and then all at once, but the compression. A model that has processed enough language has, in some sense, internalized the structure of human concern. Not knowledge exactly — more like the shape of knowledge. The topology of what we find important.
“A word is not a meaning. It is a vector in a space shaped by every context in which it has appeared.”
You cannot understand “grief” by reading its dictionary definition. You understand it through accumulation — through its proximity to “loss” and “time” and “unexpected.” Through the sentences that surround it, the words that follow.
Language is not a code. It is a residue of experience. And NLP, at its best, is an attempt to read that residue carefully.