What is Multi-Head Attention?
Multi-Head Attention
Multi-Head Attention is an attention layout in Transformers that splits query, key, and value vectors into multiple subspaces, allowing the model to attend to information from different representation coordinates simultaneously.
Why It Matters for AI Builders
Key to managing sequence memory and token weights during transformer block operations, sequence correlation mapping, and llm design; optimizing Multi-Head Attention prevents attention processing bottlenecks and keeps execution latencies low.
Detailed Deep Dive
Multi-head attention is the core component of the Transformer architecture. It splits the input vectors into multiple subspaces, allowing the model to perform attention calculations in parallel across multiple heads. This enables the network to attend to information from different representation subspaces and positions simultaneously, capturing complex context.
Frequently Asked Questions
Q:Why use multiple attention heads instead of one?
A single attention head averages out focus. Multi-head attention allows the model to simultaneously look at different tokens (e.g. grammar structure and semantic pronouns).
Q:What is the output of the multi-head attention layer?
The concatenated outputs of each individual attention head, projected back to the original embedding size.
Quick Facts
- CategoryNeural Architectures
- Key ApplicationTransformer block operations, sequence correlation mapping, and LLM design.
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