@inproceedings{wang-etal-2025-uipe,
    title = "{UIPE}: Enhancing {LLM} Unlearning by Removing Knowledge Related to Forgetting Targets",
    author = "Wang, Wenyu  and
      Zhang, Mengqi  and
      Ye, Xiaotian  and
      Ren, Zhaochun  and
      Ren, Pengjie  and
      Chen, Zhumin",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1374/",
    doi = "10.18653/v1/2025.findings-emnlp.1374",
    pages = "25212--25227",
    ISBN = "979-8-89176-335-7",
    abstract = "Large Language Models (LLMs) inevitably acquire harmful information during training on massive datasets. LLM unlearning aims to eliminate the influence of such harmful information while maintaining the model{'}s overall performance. Existing unlearning methods, represented by gradient ascent-based approaches, primarily focus on forgetting target data while overlooking the crucial impact of logically related knowledge on the effectiveness of unlearning. In this paper, through both theoretical and experimental analyses, we first demonstrate that a key reason for the suboptimal unlearning performance is that models can reconstruct the target content through reasoning with logically related knowledge. To address this issue, we propose Unlearning Improvement via Parameter Extrapolation (UIPE), a method that removes knowledge highly correlated with the forgetting targets. Experimental results show that UIPE significantly enhances the performance of GA-based method and its variants on the TOFU and WMDP benchmarks."
}