Towards Evaluation for Real-World LLM Unlearning

• URL: http://arxiv.org/abs/2508.01324v1
• Date: Sat, 02 Aug 2025 11:32:41 GMT
• Title: Towards Evaluation for Real-World LLM Unlearning
• Authors: Ke Miao, Yuke Hu, Xiaochen Li, Wenjie Bao, Zhihao Liu, Zhan Qin, Kui Ren,
• Abstract summary: We propose a new metric called Distribution Correction-based Unlearning Evaluation (DCUE)<n>It identifies core tokens and corrects distributional biases in their confidence scores using a validation set.<n>Results are quantified using the Kolmogorov-Smirnov test.
• Score: 16.31710864838019
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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