Some Intuition on Attention and the Transformer
Eugene Yan
An article explains how attention mechanisms and Transformer models work, addressing concepts like query-key-value vectors, encoder-decoder architecture, and the purpose of multiple attention heads and layers. Key mechanisms include softmax attention scoring that sums to 1, skip connections that preserve input information, and parallel processing of entire sentences rather than sequential recurrent approaches. The design enables longer-range dependencies and broader receptive fields compared to earlier encoder-decoder models that compressed information into fixed-size vectors.
Why it matters
What's the big deal, intuition on query-key-value vectors, multiple heads, multiple layers, and more.
