Listen: Attention
The transformer mechanism that lets a model weigh how much every token relates to every other, in parallel — the core of modern LLMs.
Transcript
Attention is the engine that drives modern language models. Introduced in 2017, this mechanism powers the Transformer architecture, which is the foundation of today's artificial intelligence.
Before attention, models had to process text step-by-step, word by word. Attention changed everything by looking at an entire sequence of text all at once. It calculates how much each word relates to every other word in a sentence, no matter how far apart they are. This simultaneous processing makes training incredibly fast and highly parallel.
To make this process stable and expressive, researchers use a few key refinements. Scaled dot-product attention keeps the mathematical gradients stable during training, while multi-head attention allows the model to look at the text from several different perspectives at the same time.
The only real downside to attention is its cost. Because every word is compared to every other word, the computational power required grows quadratically with the length of the input. This high cost is why researchers are constantly developing new methods to make attention more efficient for longer texts. Ultimately, understanding attention is the key to understanding how machines actually read and make sense of our language.