Pre-trained Language Models such as BERT are impressive machines with the ability to memorize, possibly generalized learning examples. We present here a small, focused contribution to the analysis of the interplay between memorization and performance of BERT in downstream tasks. We propose PreCog, a measure for evaluating memorization from pre-training, and we analyze its correlation with the BERT's performance. Our experiments show that highly memorized examples are better classified, suggesting memorization is an essential key to success for BERT.

翻译：诸如BERT等预训练语言模型具有惊人的记忆力，能够记忆可能存在泛化能力的训练样本。本文对BERT在下游任务中记忆与性能之间相互作用的分析，提供了一项小而聚焦的贡献。我们提出PreCog指标以评估预训练阶段的记忆能力，并分析其与BERT性能之间的相关性。实验表明，高记忆度的样本分类效果更佳，这暗示记忆是BERT成功的关键要素之一。