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        <title>Generalizable Lifelong Model Editing via Preference Optimization</title>
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    <abstract>Knowledge editing enables rapid updates of specific factual knowledge in large language models (LLMs) without full retraining. However, more realistic scenarios call for a lifelong framework that handles continual updates rather than one-off modifications. In such settings, existing editing methods often overfit to target prompts, significantly degrading both the generalization of the edited knowledge and the model’s general capabilities. To address this issue, we propose GLIME (Generalizable Lifelong Model Editing), which combines knowledge editing with preference optimization over generation behavior. GLIME further incorporates replay-based editing and a gradient constraint to preserve previously edited knowledge. Experimental results show that GLIME significantly improves knowledge generalization in lifelong editing settings while maintaining both editing performance and general capabilities.</abstract>
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%0 Conference Proceedings
%T Generalizable Lifelong Model Editing via Preference Optimization
%A Jung, Dahyun
%A Son, Suhyune
%A Lim, Heuiseok
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F jung-etal-2026-generalizable
%X Knowledge editing enables rapid updates of specific factual knowledge in large language models (LLMs) without full retraining. However, more realistic scenarios call for a lifelong framework that handles continual updates rather than one-off modifications. In such settings, existing editing methods often overfit to target prompts, significantly degrading both the generalization of the edited knowledge and the model’s general capabilities. To address this issue, we propose GLIME (Generalizable Lifelong Model Editing), which combines knowledge editing with preference optimization over generation behavior. GLIME further incorporates replay-based editing and a gradient constraint to preserve previously edited knowledge. Experimental results show that GLIME significantly improves knowledge generalization in lifelong editing settings while maintaining both editing performance and general capabilities.
%U https://aclanthology.org/2026.emnlp-main.170/
%P 3899-3920

Markdown (Informal)

[Generalizable Lifelong Model Editing via Preference Optimization](https://aclanthology.org/2026.emnlp-main.170/) (Jung et al., EMNLP 2026)

• Generalizable Lifelong Model Editing via Preference Optimization (Jung et al., EMNLP 2026)

ACL

• Dahyun Jung, Suhyune Son, and Heuiseok Lim. 2026. Generalizable Lifelong Model Editing via Preference Optimization. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 3899–3920, Budapest, Hungary. Association for Computational Linguistics.