OpenUnlearning: Accelerating LLM Unlearning via Unified Benchmarking of Methods and Metrics

Zico Kolter (Carnegie Mellon University) · Zachary Lipton (Carnegie Mellon University / Abridge) · Pratyush Maini (Carnegie Mellon University/ DatologyAI) · Wenlong Zhao (University of Massachusetts Amherst) · Vineeth Dorna (University of Massachusetts at Amherst) · Anmol Mekala (University of Massachusetts at Amherst) · Andrew McCallum (UMass Amherst)

benchmarking frameworkcommunity-driven pathwaycomparative analysisdata privacy evaluation metrics faithfulnessforgetting behaviorsmeta-evaluation benchmark model safetyopenunlearningregulatory compliancereproducibilityrobust unlearningunlearning algorithms

Robust unlearning is crucial for safely deploying large language models (LLMs) in environments where data privacy, model safety, and regulatory compliance must be ensured. Yet the task is inherently challenging, partly due to difficulties in reliably measuring whether unlearning has truly occurred. Moreover, fragmentation in current methodologies and inconsistent evaluation metrics hinder comparative analysis and reproducibility. To unify and accelerate research efforts, we introduce OpenUnlearning, a standardized and extensible framework designed explicitly for benchmarking both LLM unlearning methods and metrics. OpenUnlearning integrates 13 state-of-the-art unlearning algorithms and 16 diverse evaluations across 3 leading benchmarks (TOFU, MUSE, and WMDP) and also enables analyses of forgetting behaviors across 450+ publicly released checkpoints. Leveraging OpenUnlearning, we propose a novel meta-evaluation benchmark focused specifically on assessing the faithfulness and robustness of evaluation metrics themselves. We also benchmark diverse unlearning methods and provide a comparative analysis against an extensive evaluation suite. Overall, we establish a clear, community-driven pathway toward rigorous development in LLM unlearning research.