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    <abstract>Large language models (LLMs) struggle with maintaining accurate knowledge due to conflicting/outdated parametric memories. While locate-and-edit methods address this, their reliance on models’ internal representations leads to robustness failures in long-context reasoning and paraphrased queries. We identify a fundamental limitation of locate-and-edit methods: existing semantic keys (for memory localization) cannot simultaneously satisfy robustness (context-invariant activation) and specificity (precise knowledge discrimination). Through theoretical error-bound analysis, we establish formal criteria for effective editing.Our solution introduces Robust Edit Pathway (REP), a plug-and-play module that: (1) disentangles editing keys from native model representations; (2) dynamically adjusts keys via contrastive learning to achieve robustness-specificity balance. Extensive experiments across various editing methods (ROME/MEMIT/R-ROME/EMMET), existing LLMs (LLaMA2, QWen, Mistral), and datasets (CounterFact, ZsRE) show that REP improves success rate over robustness tests by up-to 66.4% while maintaining the success rate unaffected.</abstract>
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%0 Conference Proceedings
%T Keys to Robust Edits: From Theoretical Insights to Practical Advances
%A Yan, Jianhao
%A Wang, Futing
%A Luo, Yun
%A Li, Yafu
%A Zhang, Yue
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F yan-etal-2025-keys
%X Large language models (LLMs) struggle with maintaining accurate knowledge due to conflicting/outdated parametric memories. While locate-and-edit methods address this, their reliance on models’ internal representations leads to robustness failures in long-context reasoning and paraphrased queries. We identify a fundamental limitation of locate-and-edit methods: existing semantic keys (for memory localization) cannot simultaneously satisfy robustness (context-invariant activation) and specificity (precise knowledge discrimination). Through theoretical error-bound analysis, we establish formal criteria for effective editing.Our solution introduces Robust Edit Pathway (REP), a plug-and-play module that: (1) disentangles editing keys from native model representations; (2) dynamically adjusts keys via contrastive learning to achieve robustness-specificity balance. Extensive experiments across various editing methods (ROME/MEMIT/R-ROME/EMMET), existing LLMs (LLaMA2, QWen, Mistral), and datasets (CounterFact, ZsRE) show that REP improves success rate over robustness tests by up-to 66.4% while maintaining the success rate unaffected.
%R 10.18653/v1/2025.acl-long.1099
%U https://aclanthology.org/2025.acl-long.1099/
%U https://doi.org/10.18653/v1/2025.acl-long.1099
%P 22545-22560

Markdown (Informal)

[Keys to Robust Edits: From Theoretical Insights to Practical Advances](https://aclanthology.org/2025.acl-long.1099/) (Yan et al., ACL 2025)

• Keys to Robust Edits: From Theoretical Insights to Practical Advances (Yan et al., ACL 2025)

ACL

• Jianhao Yan, Futing Wang, Yun Luo, Yafu Li, and Yue Zhang. 2025. Keys to Robust Edits: From Theoretical Insights to Practical Advances. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 22545–22560, Vienna, Austria. Association for Computational Linguistics.