Understanding Multi-Head Attention in Transformers

#ai #machinelearning

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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