Transformer Attention Explained: What Do Q, K, and V Do?

What do queries, keys, and values actually do? Follow a sentence through the attention calculation, from dot products and scaled scores to attention weights and a weighted sum of values. Then compute every position in parallel, apply a causal mask, compare multiple heads, and place the complete calculation inside a GPT-style decoder. English narration; separate English and Simplified Chinese captions. CHAPTERS 00:00 Why attention needs Q, K, and V 01:41 How queries and keys produce weights 04:26 How values combine context 06:18 All positions and the causal mask 08:20 Multiple heads and the decoder The example uses 6 teaching groups and hand-set 2D vectors and matrices. These groups are not a real tokenizer result. Attention weights describe this calculation's weighting of input positions; they are not next-token probabilities or proof of causal importance. The resulting context-vector coordinates are learned features, rather than fixed named meanings. REFERENCES Attention Is All You Need — Section 3.2 https://arxiv.org/abs/1706.03762 Language Models are Few-Shot Learners — GPT3 configuration, Table 2.1 https://arxiv.org/abs/2005.14165 Jay Alammar — The Illustrated Transformer https://jalammar.github.io/illustrate... Sebastian Raschka — Self-attention from scratch https://sebastianraschka.com/blog/202... CREDITS Original mathematical diagrams and animation. The GPT lesson cover referenced in the opening is an original series graphic. English narration: Chloe, a Xiaomi MiMo preset synthetic voice. No voice cloning. Music: "Easy Lemon" by Kevin MacLeod (incompetech.com). Licensed under Creative Commons: By Attribution 4.0. https://creativecommons.org/licenses/... Track: https://incompetech.com/music/royalty... Music is looped, faded in and out, and lowered beneath the narration. #Attention #Transformers #QKV