Multi-Head Attention
Definition
Multi-Head Attention processes information in parallel from multiple learned perspectives.
Attention with multiple parallel learned projection heads that can process different representation subspaces.
Simply explained
Several computational units consider different contexts and then combine their results.
Why it matters in practice
Helps realistically assess model quality, limits, and technical decisions.
Example from business
Multiple query, key, and value projections are computed in parallel, merged, and re-projected.
Limits and pitfalls
- Automatically viewing heads as uniquely interpretable specialists or ignoring KV memory requirements.
Origin & history
Vaswani and co-authors introduced Multi-Head Attention in the Transformer in 2017.
Use in mid-sized companies
- Possible application: Multiple query, key, and value projections are computed in parallel, merged, and re-projected.
Frequently asked questions
What does Multi-Head Attention mean?
Attention with multiple parallel learned projection heads that can process different representation subspaces.
Which mistakes should be avoided?
Automatically viewing heads as uniquely interpretable specialists or ignoring KV memory requirements.
Sources
Source verified on 10.10.2026. Editorial application examples are hypothetical; no proof of real customer results.
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