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.
Frequently Asked Questions
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).
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.
Coverage Trend12 Weeks
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