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    <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knowledge in Large Language Models (LLMs) in a timely manner, thereby preventing undesirable behaviors stemming from outdated, inaccurate, or incomplete knowledge.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.</abstract>
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%0 Conference Proceedings
%T RuleEdit: Towards Rule-Level Knowledge Generalization to Mitigate Over-Editing in Large Language Models
%A Zhou, Bihan
%A Ren, HaoPeng
%A Yuan, Li
%A Cai, Yi
%A Cao, Liuwen
%A Deng, Zikun
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F zhou-etal-2025-ruleedit
%X 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knowledge in Large Language Models (LLMs) in a timely manner, thereby preventing undesirable behaviors stemming from outdated, inaccurate, or incomplete knowledge.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.
%R 10.18653/v1/2025.findings-acl.164
%U https://aclanthology.org/2025.findings-acl.164/
%U https://doi.org/10.18653/v1/2025.findings-acl.164
%P 3159-3175

Markdown (Informal)

[RuleEdit: Towards Rule-Level Knowledge Generalization to Mitigate Over-Editing in Large Language Models](https://aclanthology.org/2025.findings-acl.164/) (Zhou et al., Findings 2025)

• RuleEdit: Towards Rule-Level Knowledge Generalization to Mitigate Over-Editing in Large Language Models (Zhou et al., Findings 2025)

ACL

• Bihan Zhou, HaoPeng Ren, Li Yuan, Yi Cai, Liuwen Cao, and Zikun Deng. 2025. RuleEdit: Towards Rule-Level Knowledge Generalization to Mitigate Over-Editing in Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2025, pages 3159–3175, Vienna, Austria. Association for Computational Linguistics.