Attention is a 3 matrix product, s(QK)V where s is softmax. Each matrix has as many rows (Q and V) or columns (K) as many tokens you have in your context. The p

Attention is a 3 matrix product, s(QK)V where s is softmax. Each matrix has as many rows (Q and V) or columns (K) as many tokens you have in your context. The plot looks at the processing of a single row of Q (predicting a single token from previous ones) called q. q is a 2 element vector and is visualised as the draggable dot (imagine a line from the origin to the dot). The K matrix is shown as green dots, each previous token in the context window is represented as a separate dot. The distance of a blue dot from a corresponding green dot represents how much information from that token gets mixed into the output of the query. The green dots form a hypersphere, a 1D manifold in 2D space. In a real network it would be more like e.g. a 127D manifold in 128D space but the analogy works there as well. You can see how the query gathers information stored on the surface of the manifold by specifying a region and volume of space specified through q's direction and magnitude respectively.