@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://preview.aclanthology.org/author-page-yu-wang-polytechnic/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: **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."
}