Why "White Okra" Context Matters
In natural language, the token "Okra" isolated carries ambiguous physical properties. When passed through multi-head self-attention alongside "White", Query-Key vectors align to shift the contextual embedding trajectory toward rare botanical cultivars, demonstrating how attention contextualizes raw token embeddings.
Multi-Head Attention Projections
Multiple attention heads project embeddings into distinct subspaces simultaneously. While Head 1 focuses on modifier relationships ("White" → "Okra"), Head 2 captures action-object pairings ("Harvest" → "Okra"), allowing LLMs to process syntactical and semantic relations in parallel.
Residual Paths & Layer Normalization
The residual connection ($X + \text{SelfAttention}(X)$) preserves original positional token information, preventing vanishing gradients. LayerNorm stabilizes vector scale across transformer depth, ensuring smooth optimization across deep architectures.