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.

翻译：大规模网络语料训练的语言模型可能记忆并复现敏感或私有数据，引发法律与伦理争议。遗忘学习（即调整模型以遗忘训练数据中的特定信息）为训练后保护隐私数据提供了可行方案。尽管现有多种遗忘方法，但尚不明确这些方法在多大程度上能使模型等同于从未学习过待遗忘数据的模型。针对这一挑战，我们提出TOFU（虚构遗忘任务）基准测试，旨在深化对遗忘机制的理解。我们构建了包含200个多样化虚构作者档案的数据集，每个档案含20组问答对，并选取其中特定子集作为遗忘目标。我们设计了一套综合评价指标，从多维度评估遗忘效果，并提供了现有遗忘算法的基线结果。值得注意的是，所有基线方法均未能实现有效遗忘，这凸显了亟需开发能够真正使模型行为如同从未接触过待遗忘数据的遗忘方法。