Multi-Head Attention

Medium

llms Google DeepMind Meta

Multiple Choice

It reduces the computational cost by parallelizing attention across heads It allows the model to capture different types of relationships by attending to different representation subspaces simultaneously It increases the context window length proportionally to the number of heads It eliminates the need for positional encoding

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