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Comparison of Dense and Sparse Attention Matrices
The structure of the attention weight matrix, , is a primary differentiator between attention mechanisms. This matrix determines how the output is computed as a weighted sum of Value vectors () via the general attention formula: In standard dense attention, the matrix is fully populated with non-zero values that all contribute to the output. Conversely, sparse attention is based on the premise that most entries in the matrix can be treated as zero, with only a select subset of non-zero weights being used in the computation.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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