This article presents a theoretical framework on the memorization and performance dynamics of large-scale transformers
It is theorized that as the number of parameters grows, the model tends to memorize specific patterns rather than generalize efficiently
To understand this phenomenon, the memorization process is analyzed and a lower bound is established for the cross-entropy that a model can achieve
The results show that more parameters do not guarantee continuous improvement in performance and that there is a saturation point where model complexity exceeds the benefit in predictive capacity
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