Easy Data Unlearning Bench
On the map: LLM safety and security → Machine unlearning
Abstract (arXiv)
Evaluating machine unlearning methods remains technically challenging, with recent benchmarks requiring complex setups and significant engineering overhead. We introduce a unified and extensible benchmarking suite that simplifies the evaluation of unlearning algorithms using the KLoM (KL divergence of Margins) metric. Our framework provides precomputed model ensembles, oracle outputs, and streamlined infrastructure for running evaluations out of the box. By standardizing setup and metrics, it enables reproducible, scalable, and fair comparison across unlearning methods. We aim for this benchmark to serve as a practical foundation for accelerating research and promoting best practices in machine unlearning. Our code and data are publicly available.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Machine unlearning, one of 12 directions in LLM safety and security. Builds on 7 of its 9 references that are in the map. First posted on arXiv 18 Feb 2026.
Builds on
7 of 9 references.
Closest ancestors · 3
Same direction, newest first
- 2024Machine Unlearning Fails to Remove Data Poisoning Attacks25 Jun
- 2024Inexact Unlearning Needs More Careful Evaluations to Avoid a False Sense of Privacy2 Mar
- 2024TOFU: A Task of Fictitious Unlearning for LLMs11 Jan
Other references · 4
- 2021Membership Inference Attacks From First Principlescited by 839
- 2020RobustBench: a standardized adversarial robustness benchmarkcited by 659
- 2024The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearningcited by 488
- 2024Safety Alignment Should Be Made More Than Just a Few Tokens Deepcited by 450
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