@inproceedings{baghel-etal-2025-resolving,
title = "Resolving {U}nder{E}dit {\&} {O}ver{E}dit with Iterative {\&} Neighbor-Assisted Model Editing",
author = "Baghel, Bhiman Kumar and
Jordan, Emma and
Shi, Zheyuan Ryan and
Li, Xiang Lorraine",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.798/",
doi = "10.18653/v1/2025.findings-emnlp.798",
pages = "14786--14808",
ISBN = "979-8-89176-335-7",
abstract = "Large Language Models (LLMs) are widely deployed in downstream tasks, but keeping their knowledge up-to-date via retraining or fine-tuning is often computationally expensive. Model editing provides a more efficient alternative by updating a targeted subset of parameters, which often follows the locate-and-edit paradigm. Despite this efficiency, existing methods are limited: edits may fail to inject knowledge (UnderEdit) or unintentionally disrupt unrelated neighboring knowledge (OverEdit). To address these challenges, we propose two complementary methods: \textbf{iterative model editing}, which applies successive edits to mitigate UnderEdit, and \textbf{neighbor-assisted model editing}, which incorporates neighboring knowledge during editing to reduce OverEdit. Our extensive experiments show that these techniques improve editing performance across multiple LLMs, algorithms, and benchmarks, reducing UnderEdit by up to 38 percentage points and OverEdit by up to 6, while remaining broadly applicable to any locate-and-edit method."
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<abstract>Large Language Models (LLMs) are widely deployed in downstream tasks, but keeping their knowledge up-to-date via retraining or fine-tuning is often computationally expensive. Model editing provides a more efficient alternative by updating a targeted subset of parameters, which often follows the locate-and-edit paradigm. Despite this efficiency, existing methods are limited: edits may fail to inject knowledge (UnderEdit) or unintentionally disrupt unrelated neighboring knowledge (OverEdit). To address these challenges, we propose two complementary methods: iterative model editing, which applies successive edits to mitigate UnderEdit, and neighbor-assisted model editing, which incorporates neighboring knowledge during editing to reduce OverEdit. Our extensive experiments show that these techniques improve editing performance across multiple LLMs, algorithms, and benchmarks, reducing UnderEdit by up to 38 percentage points and OverEdit by up to 6, while remaining broadly applicable to any locate-and-edit method.</abstract>
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%0 Conference Proceedings
%T Resolving UnderEdit & OverEdit with Iterative & Neighbor-Assisted Model Editing
%A Baghel, Bhiman Kumar
%A Jordan, Emma
%A Shi, Zheyuan Ryan
%A Li, Xiang Lorraine
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F baghel-etal-2025-resolving
%X Large Language Models (LLMs) are widely deployed in downstream tasks, but keeping their knowledge up-to-date via retraining or fine-tuning is often computationally expensive. Model editing provides a more efficient alternative by updating a targeted subset of parameters, which often follows the locate-and-edit paradigm. Despite this efficiency, existing methods are limited: edits may fail to inject knowledge (UnderEdit) or unintentionally disrupt unrelated neighboring knowledge (OverEdit). To address these challenges, we propose two complementary methods: iterative model editing, which applies successive edits to mitigate UnderEdit, and neighbor-assisted model editing, which incorporates neighboring knowledge during editing to reduce OverEdit. Our extensive experiments show that these techniques improve editing performance across multiple LLMs, algorithms, and benchmarks, reducing UnderEdit by up to 38 percentage points and OverEdit by up to 6, while remaining broadly applicable to any locate-and-edit method.
%R 10.18653/v1/2025.findings-emnlp.798
%U https://aclanthology.org/2025.findings-emnlp.798/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.798
%P 14786-14808
Markdown (Informal)
[Resolving UnderEdit & OverEdit with Iterative & Neighbor-Assisted Model Editing](https://aclanthology.org/2025.findings-emnlp.798/) (Baghel et al., Findings 2025)
ACL