Before it
Batch normalisation stabilised training but depended on batch statistics.
The problem
Batch statistics are awkward for variable-length sequences and small batches.
What it introduced
Normalise across the features of each example independently of the batch.
Why it matters
The other half of the transformer block's plumbing. Pre-norm placement is what makes deep stacks trainable.
What modern models inherit
The normalisation layer inside every transformer block.
What came next
Used in the Transformer, then moved before each sublayer in GPT-2.