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Gated Linear Unit (GLU)
The Gated Linear Unit (GLU) is a family of activation functions that has gained popularity for its use in Large Language Models (LLMs). The specific variant of a GLU is defined by its internal non-linear activation function, denoted as 蟽(路). For example, using the GELU function for 蟽(路) results in GeGLU, and using the Swish function results in SwiGLU.
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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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Gaussian Error Linear Unit (GELU)
Gated Linear Unit (GLU)
A machine learning engineer is analyzing the feed-forward network (FFN) component of a transformer model. They want to replace the standard Rectified Linear Unit (ReLU) activation function with a more modern alternative to potentially improve model performance. Which of the following statements best analyzes the rationale for using a function like the Gaussian Error Linear Unit (GELU) or Swish over ReLU in this context?
Match each activation function, which can be used in the feed-forward network of a transformer model, with its corresponding description.
Evaluating an Activation Function Change in a Transformer FFN
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Gated Linear Unit (GLU) Formula
GeGLU (GELU-based Gated Linear Unit)
SwiGLU (Swish-based Gated Linear Unit)
Shazeer [2020] on Gated Linear Units
Structural Analysis of Gated Linear Units
The Gated Linear Unit (GLU) architecture processes an input through two parallel linear transformations. One of these transformed outputs is then passed through a non-linear function before being combined with the other via an element-wise product. What is the analytical purpose of this non-linearly transformed path in the overall mechanism?
A standard feed-forward network layer applies a non-linear activation function after a single linear transformation. The Gated Linear Unit (GLU) architecture, however, processes an input through two parallel linear transformations, where one path acts as a 'gate' for the other after being passed through a non-linear function. What is the primary analytical advantage of this gating mechanism compared to using a single, non-gated activation function?