@inproceedings{zhou-etal-2025-ruleedit,
    title = "{R}ule{E}dit: Towards Rule-Level Knowledge Generalization to Mitigate Over-Editing in Large Language Models",
    author = "Zhou, Bihan  and
      Ren, HaoPeng  and
      Yuan, Li  and
      Cai, Yi  and
      Cao, Liuwen  and
      Deng, Zikun",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.findings-acl.164/",
    doi = "10.18653/v1/2025.findings-acl.164",
    pages = "3159--3175",
    ISBN = "979-8-89176-256-5",
    abstract = "Knowledge editing emerges as a promising approach for updating target knowledge in Large Language Models (LLMs) in a timely manner, thereby preventing undesirable behaviors stemming from outdated, inaccurate, or incomplete knowledge. However, existing methods mainly focus on instance-level editing, which is prone to over-editing risk featuring knowledge degradation and general ability deterioration, due to redundant instance-specific modifications for knowledge. To mitigate the over-editing risk, we explore the rule-level editing problem that avoids case-by-case modification by generalizing rule-level knowledge to update rule-derived instances. We further construct a benchmark called $RuleEdit$ for systematic evaluation on rule-level editing. Moreover, we propose a Rule-Transfer Editing (RTE) method to facilitate effective updates and generalizations of rule-level knowledge in LLMs. Experimental results highlight our significant improvements, with the enhancements of 28.1{\%} in portability and 8.1{\%} in average performance over the best-performing baselines for LLaMA-2-7B on $RULE_{mix}$."
}