Impact of Layer Norm on Memorization and Generalization in Transformers

The study of Layer Normalization (LayerNorm) in transformers highlights its pivotal role in both memorization and learning processes. As noted in related research, such as the exploration of analogical structures for efficient linguistic rule induction, the effectiveness of large language models often hinges on their training methodologies. The findings from the base article align with these insights, emphasizing that LayerNorm significantly stabilizes learning in Pre-LayerNorm transformers while impacting memorization in Post-LayerNorm models. This connection underscores the importance of architectural choices in optimizing model performance across various datasets.