Why is layer normalization important in transformer architectures?

Layer normalization is a critical technique in Transformer architectures, as highlighted in NVIDIA’s Generative AI and LLMs course. It stabilizes the learning process by normalizing the inputs to each layer across the features, ensuring that the mean and variance of the activations remain consistent. This is achieved by computing the mean and standard deviation of the inputs to a layer and scaling them to a standard range, which helps mitigate issues like vanishing or exploding gradients during training. This stabilization improves training efficiency and model performance, particularly in deep networks like Transformers. Option A is incorrect, as layer normalization primarily aids training stability, not generalization to new data, which is influenced by other factors like regularization. Option B is wrong, as layer normalization does not compress model size but adjusts activations. Option D is inaccurate, as positional information is handled by positional encoding, not layer normalization. The course notes: "Layer normalization stabilizes the training of Transformer models by normalizing layer inputs, ensuring consistent activation distributions and improving convergence."