Large language model (LLM) unlearning is critical in real-world applications
where it is necessary to efficiently remove the influence of private,
copyrighted, or harmful data from some users. However, existing utility-centric
unlearning metrics (based on model utility) may fail to accurately evaluate the
extent of unlearning in realistic settings such as when (a) the forget and
retain set have semantically similar content, (b) retraining the model from
scratch on the retain set is impractical, and/or (c) the model owner can
improve the unlearning metric without directly performing unlearning on the
LLM. This paper presents the first data-centric unlearning metric for LLMs
called WaterDrum that exploits robust text watermarking for overcoming these
limitations. We also introduce new benchmark datasets for LLM unlearning that
contain varying levels of similar data points and can be used to rigorously
evaluate unlearning algorithms using WaterDrum. Our code is available at
https://github.com/lululu008/WaterDrum and our new benchmark datasets are
released at https://huggingface.co/datasets/Glow-AI/WaterDrum-Ax.