Modern neural language models widely used in tasks across NLP risk memorizing
sensitive information from their training data. As models continue to scale up
in parameters, training data, and compute, understanding memorization in
language models is both important from a learning-theoretical point of view,
and is practically crucial in real world applications. An open question in
previous studies of memorization in language models is how to filter out
"common" memorization. In fact, most memorization criteria strongly correlate
with the number of occurrences in the training set, capturing "common"
memorization such as familiar phrases, public knowledge or templated texts. In
this paper, we provide a principled perspective inspired by a taxonomy of human
memory in Psychology. From this perspective, we formulate a notion of
counterfactual memorization, which characterizes how a model's predictions
change if a particular document is omitted during training. We identify and
study counterfactually-memorized training examples in standard text datasets.
We further estimate the influence of each training example on the validation
set and on generated texts, and show that this can provide direct evidence of
the source of memorization at test time.

本文提出了一种源于心理学中人类记忆分类的对抗性记忆学习方法，进行神经语言模型的训练数据筛选，以减少其对训练数据中的敏感信息的记忆，并探究其训练样本中的对抗性记忆，从而提供一种可用于测试数据及其生成的文本来源推定的直接证据。