Pretrained Language Models (LMs) memorize a vast amount of knowledge during
initial pretraining, including information that may violate the privacy of
personal lives and identities. Previous work addressing privacy issues for
language models has mostly focused on data preprocessing and differential
privacy methods, both requiring re-training the underlying LM. We propose
knowledge unlearning as an alternative method to reduce privacy risks for LMs
post hoc. We show that simply applying the unlikelihood training objective to
target token sequences is effective at forgetting them with little to no
degradation of general language modeling performances; it sometimes even
substantially improves the underlying LM with just a few iterations. We also
find that sequential unlearning is better than trying to unlearn all the data
at once and that unlearning is highly dependent on which kind of data (domain)
is forgotten. By showing comparisons with a previous data preprocessing method
known to mitigate privacy risks for LMs, we show that unlearning can give a
stronger empirical privacy guarantee in scenarios where the data vulnerable to
extraction attacks are known a priori while being orders of magnitude more
computationally efficient. We release the code and dataset needed to replicate
our results at https://github.com/joeljang/knowledge-unlearning