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Attention Weight Matrix (α)
The attention weight matrix, denoted as , contains the weights that determine the importance of each value vector for a given query. This matrix is derived from the query () and key () matrices and has dimensions of , where is the number of items in the input sequence.
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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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Attention Weight Matrix (α)
Sparse Attention
Self-attention layers' first approach
In a general attention mechanism, the output is calculated as a weighted sum of the Value vectors, where the weights are determined by the interaction between Query and Key vectors. The standard formula is: . Consider a scenario where this formula is mistakenly altered to be: . What is the most significant consequence of this modification?
Dimensional Analysis of the Attention Formula
Applying the Attention Mechanism Roles
Self-Attention Output Formula for a Single Query
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Causal Attention Weight Matrix Calculation
An attention mechanism processes the input sequence:
['The', 'robot', 'grasped', 'the', 'wrench']. The attention weight matrix is calculated to determine the contextual importance of each word. The row in the matrix corresponding to the word 'grasped' has the highest weight value in the column corresponding to the word 'wrench'. What does this high weight signify?Interpreting an Attention Weight Matrix
In an attention mechanism processing a sequence of
mitems, anm x mattention weight matrix is generated. What does thei-th row of this matrix fundamentally represent?Query-Key-Value Attention Output Matrix Product