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Each attention head learns different linear projections of the Q, K, and V matrices. This allows the model to capture different aspects of the relationships between words in the sequence simultaneously, rather than focusing on a single aspect. By doing this, multi-head attention ensures that the input embeddings are updated from a more varied ...
Concretely, let the multiple attention heads be indexed by , then we have (,,) = [] ((,,)) where the matrix is the concatenation of word embeddings, and the matrices ,, are "projection matrices" owned by individual attention head , and is a final projection matrix owned by the whole multi-headed attention head.
During the deep learning era, attention mechanism was developed to solve similar problems in encoding-decoding. [1]In machine translation, the seq2seq model, as it was proposed in 2014, [24] would encode an input text into a fixed-length vector, which would then be decoded into an output text.
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Additional research proposes the notion of a moveable filter. The multimode theory of attention combines physical and semantic inputs into one theory. Within this model, attention is assumed to be flexible, allowing different depths of perceptual analysis. [28] Which feature gathers awareness is dependent upon the person's needs at the time. [3]