Large language models trained on massive corpora of data from the web can
memorize and reproduce sensitive or private data raising both legal and ethical
concerns. Unlearning, or tuning models to forget information present in their
training data, provides us with a way to protect private data after training.
Although several methods exist for such unlearning, it is unclear to what
extent they result in models equivalent to those where the data to be forgotten
was never learned in the first place. To address this challenge, we present
TOFU, a Task of Fictitious Unlearning, as a benchmark aimed at helping deepen
our understanding of unlearning. We offer a dataset of 200 diverse synthetic
author profiles, each consisting of 20 question-answer pairs, and a subset of
these profiles called the forget set that serves as the target for unlearning.
We compile a suite of metrics that work together to provide a holistic picture
of unlearning efficacy. Finally, we provide a set of baseline results from
existing unlearning algorithms. Importantly, none of the baselines we consider
show effective unlearning motivating continued efforts to develop approaches
for unlearning that effectively tune models so that they truly behave as if
they were never trained on the forget data at all.

利用大规模语言模型训练数据时，即使属敏感或私密的数据也是可以被记忆和复制的，引发了法律和伦理方面的担忧。为了保护个人数据，我们提出了 TOFU（Task of Fictitious Unlearning）作为一个基准，以帮助深入理解遗忘的过程。通过提供一套综合指标，我们评估了现有遗忘算法的基准结果，并指出需要继续努力开发能够真正使模型忘却目标数据的遗忘方法。