Walk Through Multi-Head Attention
Explain how multi-head attention runs several learned attention projections and recombines them.
Explain multi-head attention without calling heads voters or mini-models. Separate the parallel subspaces: project, attend, concatenate, output-project. The common trap is saying each head votes on the final answer. Heads produce intermediate contextual vectors, not final text decisions. Step 1: Project per head The same token representations are sent through different learned Q, K, and V projections for each head. This creates separate compatibility and value spaces. Step 2: Attend in parallel Each head computes its own attention weights and value mixture. Different heads can route different patterns, although not every head has a clean human label. Step 3: Recombine…
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