Self-attention already helps a transformer understand relationships between words using Query, Key, and Value. But there’s a problem.
One attention mechanism usually ends up focusing on a limited kind of relationship at a time.
Language doesn’t work like that. A sentence can have structure, meaning, and long-range links all at once.
That’s why transformers use multi-head attention.
What happens in multi-head attention
Instead of doing attention once, the model does it multiple times in parallel.
Each run is called a head, and each head has its own learned weights for Query, Key, and Value.
So every head looks at the same sentence, but in its own way.
How it flows
- The input embeddings are first prepared
- They are split into multiple heads using linear projections
- Each head runs its own self-attention
- Each head produces its own output
- All outputs are joined back together
- A final layer mixes them into one result
Why this works better compared to previous approach
Different heads naturally pick up different things:
- word order and grammar
- nearby word relationships
- long-distance links
- meaning-based connections
So instead of forcing one attention mechanism to do everything, the model spreads the job across multiple perspectives.
One head is like reading a sentence with one focus.
Multiple heads is like reading it several times, each time noticing something different, then combining those notes.
Multi-head attention doesn’t change the idea of self-attention. It just runs it multiple times in parallel so the model can understand language from different angles at once.
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