What is the purpose of layer normalization in transformer architectures?

Layer normalization is a technique used in transformer architectures to stabilize and improve the training process by normalizing the outputs of each layer across the features, instead of across the batch. This helps to mitigate issues related to internal covariate shift, where the distribution of layer inputs changes during training. By ensuring that the mean and variance of inputs to the layer are consistent, layer normalization enhances convergence speed, reduces sensitivity to hyperparameters, and often leads to better overall performance. It is especially beneficial in deep networks, where managing gradients is crucial.