Query, Key and Value vectors in Transformer Neural Networks

3-Second Blueprint (English)

1. Each token projects into Query (what it seeks), Key (what it offers for matching), and Value (its content) vectors.
2. Scaled dot-product scores between Queries and Keys pass through optional causal masking and softmax to form attention weights.
3. Multiplying attention weights by Value vectors produces context-enriched representations across the sequence.