@inproceedings{feng-etal-2025-geoedit,
title = "{G}eo{E}dit: Geometric Knowledge Editing for Large Language Models",
author = "Feng, Yujie and
Zhan, Li-Ming and
Lu, Zexin and
Xu, Yongxin and
Chu, Xu and
Wang, Yasha and
Cao, Jiannong and
Yu, Philip S. and
Wu, Xiao-Ming",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.676/",
pages = "13401--13416",
ISBN = "979-8-89176-332-6",
abstract = "Regular updates are essential for maintaining up-to-date knowledge in large language models (LLMs). However, existing training-based model editing methods often struggle to effectively incorporate new knowledge while preserving unrelated general knowledge. To address this challenge, we propose a novel framework called Geometric Knowledge Editing (GeoEdit). GeoEdit utilizes the geometric relationships of parameter updates from fine-tuning to differentiate between neurons associated with new knowledge updates and those related to general knowledge perturbations. By employing a direction-aware knowledge identification method, we avoid updating neurons with directions approximately orthogonal to existing knowledge, thus preserving the model{'}s generalization ability. For the remaining neurons, we integrate both old and new knowledge for aligned directions and apply a ``forget-then-learn'' editing strategy for opposite directions. Additionally, we introduce an importance-guided task vector fusion technique that filters out redundant information and provides adaptive neuron-level weighting, further enhancing model editing performance. Extensive experiments on two publicly available datasets demonstrate the superiority of GeoEdit over existing state-of-the-art methods."
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<abstract>Regular updates are essential for maintaining up-to-date knowledge in large language models (LLMs). However, existing training-based model editing methods often struggle to effectively incorporate new knowledge while preserving unrelated general knowledge. To address this challenge, we propose a novel framework called Geometric Knowledge Editing (GeoEdit). GeoEdit utilizes the geometric relationships of parameter updates from fine-tuning to differentiate between neurons associated with new knowledge updates and those related to general knowledge perturbations. By employing a direction-aware knowledge identification method, we avoid updating neurons with directions approximately orthogonal to existing knowledge, thus preserving the model’s generalization ability. For the remaining neurons, we integrate both old and new knowledge for aligned directions and apply a “forget-then-learn” editing strategy for opposite directions. Additionally, we introduce an importance-guided task vector fusion technique that filters out redundant information and provides adaptive neuron-level weighting, further enhancing model editing performance. Extensive experiments on two publicly available datasets demonstrate the superiority of GeoEdit over existing state-of-the-art methods.</abstract>
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%0 Conference Proceedings
%T GeoEdit: Geometric Knowledge Editing for Large Language Models
%A Feng, Yujie
%A Zhan, Li-Ming
%A Lu, Zexin
%A Xu, Yongxin
%A Chu, Xu
%A Wang, Yasha
%A Cao, Jiannong
%A Yu, Philip S.
%A Wu, Xiao-Ming
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F feng-etal-2025-geoedit
%X Regular updates are essential for maintaining up-to-date knowledge in large language models (LLMs). However, existing training-based model editing methods often struggle to effectively incorporate new knowledge while preserving unrelated general knowledge. To address this challenge, we propose a novel framework called Geometric Knowledge Editing (GeoEdit). GeoEdit utilizes the geometric relationships of parameter updates from fine-tuning to differentiate between neurons associated with new knowledge updates and those related to general knowledge perturbations. By employing a direction-aware knowledge identification method, we avoid updating neurons with directions approximately orthogonal to existing knowledge, thus preserving the model’s generalization ability. For the remaining neurons, we integrate both old and new knowledge for aligned directions and apply a “forget-then-learn” editing strategy for opposite directions. Additionally, we introduce an importance-guided task vector fusion technique that filters out redundant information and provides adaptive neuron-level weighting, further enhancing model editing performance. Extensive experiments on two publicly available datasets demonstrate the superiority of GeoEdit over existing state-of-the-art methods.
%U https://aclanthology.org/2025.emnlp-main.676/
%P 13401-13416
Markdown (Informal)
[GeoEdit: Geometric Knowledge Editing for Large Language Models](https://aclanthology.org/2025.emnlp-main.676/) (Feng et al., EMNLP 2025)
ACL
- Yujie Feng, Li-Ming Zhan, Zexin Lu, Yongxin Xu, Xu Chu, Yasha Wang, Jiannong Cao, Philip S. Yu, and Xiao-Ming Wu. 2025. GeoEdit: Geometric Knowledge Editing for Large Language Models. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 13401–13416, Suzhou, China. Association for Computational Linguistics.