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arXiv · 2508.01324

Towards Evaluation for Real-World LLM Unlearning

Ke Miao·Yuke Hu·Xiaochen Li·Wenjie Bao·Zhihao Liu·Zhan Qin·Kui Ren
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Abstract

This paper analyzes the limitations of existing unlearning evaluation metrics in terms of practicality, exactness, and robustness in real-world LLM unlearning scenarios. To overcome these limitations, we propose a new metric called Distribution Correction-based Unlearning Evaluation (DCUE). It identifies core tokens and corrects distributional biases in their confidence scores using a validation set. The evaluation results are quantified using the Kolmogorov-Smirnov test. Experimental results demonstrate that DCUE overcomes the limitations of existing metrics, which also guides the design of more practical and reliable unlearning algorithms in the future.

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Ke Miao, Yuke Hu, Xiaochen Li, Wenjie Bao, Zhihao Liu, Zhan Qin, Kui Ren. 2025-08-02. Towards Evaluation for Real-World LLM Unlearning. https://arxiv.org/abs/2508.01324

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