Rationale for Unique Projections in Multi-Head Attention
In the context of a multi-head attention mechanism, explain the primary reason for using distinct, learnable weight matrices to project the input representation into separate Query, Key, and Value sets for each individual attention head.
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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
Analysis in Bloom's Taxonomy
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Individual Attention Head Formula
Shape of Key Weight Matrix per Head
Shape of Key Weight Sub-Matrix per Head
In a multi-head attention mechanism with 'M' heads, an engineer makes an implementation error. Instead of creating a unique set of learnable weight matrices for the query, key, and value projections for each of the 'M' heads, the same single set of query, key, and value weight matrices is shared across all heads. What is the primary consequence of this error on the model's functionality?
Rationale for Unique Projections in Multi-Head Attention
Attention Head Specialization