Can someone clarify the self-attention mechanism? I am trying to figure out how use this specific feature to predict the next word in a sequence. How does assigning weights to different words actually translate into creating highly coherent human-like paragraphs?
3 answers
Self-attention works by creating three vectors for every input token: Queries, Keys, and Values. To determine a word's context, the model scores its Query vector against the Key vectors of all other words in the text. These scores are normalized to create attention weights, which dictate how much focus the word receives. Finally, multiplying these weights by the Value vectors produces a nuanced context vector. In generative tasks, this means the model dynamically adapts its focus to relevant preceding context words to predict the most logical next token.
Are you asking about standard bidirectional self-attention found in encoder models, or masked self-attention which is specifically used within autoregressive decoder architectures for generation?
It computes a weighted mathematical average of all word representations, letting the model focus heavily on relevant context while ignoring fluff.
Exactly, Pamela. This selective focus is what prevents the generation from drifting off-topic over long paragraphs, keeping the output logical.
Larry, I am looking specifically at the masked version used in decoders. I want to understand how masking prevents the model from looking at future tokens during training, ensuring it learns to predict subsequent text using only historical contextual data.