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Analyzing a Gating Mechanism
An activation function in a neural network processes an input by first applying two distinct linear transformations, creating two intermediate vectors. One of these vectors is then passed through a Gaussian Error Linear Unit (GELU) function. The output of this GELU function is then multiplied, element by element, with the second intermediate vector to produce the final output. Based on this structure, explain the primary role of the vector that has been processed by the GELU function.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
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Analysis in Bloom's Taxonomy
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GeGLU (GELU-based Gated Linear Unit) Formula
Applications of GeGLU in Large Language Models
An activation function is constructed by taking an input, applying two separate linear transformations to it, and then combining the results. One transformed output is passed through a non-linear 'gating' function, and the result is then multiplied element-wise with the other transformed output. For this entire structure to be correctly identified as a GeGLU, what must be true about the gating function?
Analyzing a Gating Mechanism
Analysis of a Custom Activation Unit