What is the function of the feed-forward network within each encoder and decoder layer of the Transformer?

The function of the feed-forward network (FFN) within each encoder and decoder layer of the Transformer is to introduce non-linearity and to further process the representations learned by the self-attention mechanism. While the self-attention mechanism is crucial for capturing relationships between words, it is a linear operation. Without non-linearity, the Transformer model would be limited in its ability to learn complex functions. The FFN consists of two linear transformations with a non-linear activation function in between, typically a ReLU (Rectified Linear Unit) activation. This combination allows the network to learn more complex relationships and patterns in the data. The FFN is applied to each word's representation independently, after it has been processed by the self-attention mechanism. This allows the network to learn word-specific transformations that are tailored to the context provided by the self-attention mechanism. In essence, the FFN adds expressive power to the model, allowing it to learn more intricate relationships than self-attention alone can capture. The feed-forward network typically has a larger hidden layer size than the embedding dimension, which allows it to capture more nuanced information. This expansion and subsequent reduction in dimensionality help the network to learn more robust and generalizable representations. Therefore, the FFN acts as a crucial component in the Transformer architecture by providing the necessary non-linearity to model complex relationships and enhance the representation learned by the self-attention mechanism.