WaterDrum: Watermarking for Data-centric Unlearning Metric

TL;DR

WaterDrum introduces a novel data-centric watermarking metric for evaluating large language model unlearning, addressing limitations of utility-based metrics especially with similar data and practical retraining constraints.

Contribution

The paper presents WaterDrum, the first watermarking-based unlearning metric for LLMs, along with new benchmark datasets for rigorous evaluation of unlearning methods.

Findings

WaterDrum effectively measures unlearning in realistic scenarios.

New benchmark datasets enable comprehensive evaluation.

WaterDrum outperforms utility-based metrics in certain settings.

Abstract

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. Existing utility-centric unlearning metrics (based on model utility) may fail to accurately evaluate the extent of unlearning in realistic settings such as when the forget and retain sets have semantically similar content and/or retraining the model from scratch on the retain set is impractical. This paper presents the first data-centric unlearning metric for LLMs called WaterDrum that exploits robust text watermarking to overcome these limitations. We introduce new benchmark datasets (with different levels of data similarity) for LLM unlearning that can be used to rigorously evaluate unlearning algorithms via 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.