打造简易统一的机器遗忘评估框架，加速算法比较与研究。
Easy Data Unlearning Bench
- 用KLoM指标统一评估遗忘算法，简化复杂实验流程。
- 提供预计算模型集成与接口，开箱即用无需额外配置。
- 适合研究者快速测试和对比新遗忘方法，推动领域标准化。
机器遗忘方法的评估仍面临技术挑战，现有基准需复杂设置与大量工程投入。本文提出一个统一且可扩展的基准测试套件，采用KL散度边际（KLoM）作为评估指标，简化遗忘算法的评测流程。框架提供预计算的模型集成、原始输出及简化部署环境，实现开箱即用。通过统一设置与指标，支持可复现、可扩展、公平的算法比较。本工作旨在为机器遗忘研究提供实用基础，推动最佳实践发展。代码与数据已公开。
原文摘要 · Abstract (English)
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.
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