The article explores the limitations of increasing Transformer model size, revealing that enhanced performance isn't guaranteed. It introduces a theoretical framework utilizing Hopfield networks to model Transformers as associative memories, linking the attention mechanism to a new energy function. This framework proposes that the memorization of training samples impacts generalization ability. Empirical studies involving GPT-2 and vanilla Transformers validate the theory, demonstrating insights into the dynamics of performance and generalization in these models.
This study presents a theoretical framework revealing how Transformer models, particularly through associative memories, encapsulate the dynamics of memorization and generalization in language processing.
We demonstrate that while increasing a Transformer’s size does not always yield better performance, a deeper understanding of its design through memory-based models can lead to improved outcomes.
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