@inproceedings{chen-etal-2026-relaxing,
title = "Relaxing the Constraints: A Dual-Importance Projection Mechanism for Lifelong Model Editing",
author = "Chen, Zhenghai and
Xu, Senbin and
Tan, Jiaxi and
Wu, Xinhua and
Zhang, Yan and
Zheng, Xiawu and
Zhang, Shengchuan and
Li, Ke and
Zhao, Sicheng and
Cao, Liujuan and
Ji, Rongrong",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1910/",
pages = "38308--38333",
ISBN = "979-8-89176-395-1",
abstract = "Factual knowledge stored in Large Language Models (LLMs) inevitably becomes outdated or erroneous over time, making it critical to update these models without incurring the high cost of retraining. Existing sequential knowledge editing methods predominantly rely on strict orthogonal projection to preserve previously edited knowledge. However, this excessive constraint limits gradient expressiveness, resulting in a significant degradation of model generalization and overall performance as the number of edits increases. To address this challenge, we propose Dual-Importance Projection Editing (DipEdit). This method leverages Singular Value Decomposition (SVD) to identify critical gradient subspaces and introduces a dual mechanism comprising ``accumulated importance'' and ``projection importance.'' Unlike traditional approaches that enforce strict orthogonality, DipEdit dynamically scales gradient components parallel to key subspaces based on their projection importance rather than discarding them directly. This approach enhances the model{'}s adaptability to new knowledge while maximally preserving historical knowledge. Extensive experiments conducted on five mainstream LLMs using the ZsRE and Counterfact datasets demonstrate that DipEdit effectively handles thousands of sequential edits. The proposed method achieves an average comprehensive performance improvement of 10.36{\%} and effectively maintains the model{'}s general capabilities on downstream tasks. Code is available at: https://github.com/czhhhla/DipEdit."
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<abstract>Factual knowledge stored in Large Language Models (LLMs) inevitably becomes outdated or erroneous over time, making it critical to update these models without incurring the high cost of retraining. Existing sequential knowledge editing methods predominantly rely on strict orthogonal projection to preserve previously edited knowledge. However, this excessive constraint limits gradient expressiveness, resulting in a significant degradation of model generalization and overall performance as the number of edits increases. To address this challenge, we propose Dual-Importance Projection Editing (DipEdit). This method leverages Singular Value Decomposition (SVD) to identify critical gradient subspaces and introduces a dual mechanism comprising “accumulated importance” and “projection importance.” Unlike traditional approaches that enforce strict orthogonality, DipEdit dynamically scales gradient components parallel to key subspaces based on their projection importance rather than discarding them directly. This approach enhances the model’s adaptability to new knowledge while maximally preserving historical knowledge. Extensive experiments conducted on five mainstream LLMs using the ZsRE and Counterfact datasets demonstrate that DipEdit effectively handles thousands of sequential edits. The proposed method achieves an average comprehensive performance improvement of 10.36% and effectively maintains the model’s general capabilities on downstream tasks. Code is available at: https://github.com/czhhhla/DipEdit.</abstract>
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%0 Conference Proceedings
%T Relaxing the Constraints: A Dual-Importance Projection Mechanism for Lifelong Model Editing
%A Chen, Zhenghai
%A Xu, Senbin
%A Tan, Jiaxi
%A Wu, Xinhua
%A Zhang, Yan
%A Zheng, Xiawu
%A Zhang, Shengchuan
%A Li, Ke
%A Zhao, Sicheng
%A Cao, Liujuan
%A Ji, Rongrong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F chen-etal-2026-relaxing
%X Factual knowledge stored in Large Language Models (LLMs) inevitably becomes outdated or erroneous over time, making it critical to update these models without incurring the high cost of retraining. Existing sequential knowledge editing methods predominantly rely on strict orthogonal projection to preserve previously edited knowledge. However, this excessive constraint limits gradient expressiveness, resulting in a significant degradation of model generalization and overall performance as the number of edits increases. To address this challenge, we propose Dual-Importance Projection Editing (DipEdit). This method leverages Singular Value Decomposition (SVD) to identify critical gradient subspaces and introduces a dual mechanism comprising “accumulated importance” and “projection importance.” Unlike traditional approaches that enforce strict orthogonality, DipEdit dynamically scales gradient components parallel to key subspaces based on their projection importance rather than discarding them directly. This approach enhances the model’s adaptability to new knowledge while maximally preserving historical knowledge. Extensive experiments conducted on five mainstream LLMs using the ZsRE and Counterfact datasets demonstrate that DipEdit effectively handles thousands of sequential edits. The proposed method achieves an average comprehensive performance improvement of 10.36% and effectively maintains the model’s general capabilities on downstream tasks. Code is available at: https://github.com/czhhhla/DipEdit.
%U https://aclanthology.org/2026.findings-acl.1910/
%P 38308-38333
Markdown (Informal)
[Relaxing the Constraints: A Dual-Importance Projection Mechanism for Lifelong Model Editing](https://aclanthology.org/2026.findings-acl.1910/) (Chen et al., Findings 2026)
ACL
- Zhenghai Chen, Senbin Xu, Jiaxi Tan, Xinhua Wu, Yan Zhang, Xiawu Zheng, Shengchuan Zhang, Ke Li, Sicheng Zhao, Liujuan Cao, and Rongrong Ji. 2026. Relaxing the Constraints: A Dual-Importance Projection Mechanism for Lifelong Model Editing. In Findings of the Association for Computational Linguistics: ACL 2026, pages 38308–38333, San Diego, California, United States. Association for Computational Linguistics.