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    <abstract>Large language models (LLMs) store extensive factual knowledge acquired during pretraining, yet this knowledge is inherently static and may become inaccurate or outdated, leading to knowledge hallucinations. Knowledge editing offers an efficient alternative to full retraining by enabling targeted factual updates while preserving overall model behavior. Existing locate-then-edit methods, however, rely on fixed layer selection strategies, treating the locating stage as a static design choice and failing to account for the hierarchical and instance-dependent nature of knowledge representation in LLMs. In this paper, we propose FiDAL, a Fisher-driven adaptation-aware locating strategy that dynamically identifies which model components should be edited for a given knowledge update. FiDAL formulates localization as a weight-level decision problem and leverages Fisher Information to select layers that are both influential and sensitive to factual modifications. A lightweight probing stage with low-rank modulation enables efficient localization with minimal overhead. Experiments on standard benchmarks demonstrate that FiDAL consistently improves editing effectiveness and knowledge preservation across multiple editing methods.</abstract>
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%0 Conference Proceedings
%T Fisher-Driven Adaptive Locating for Knowledge Editing in Large Language Models
%A Xu, Chenghao
%A Yan, Jiexi
%A Lyu, Guangtao
%A Liu, Qi
%A Yang, Muli
%A Deng, Cheng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F xu-etal-2026-fisher
%X Large language models (LLMs) store extensive factual knowledge acquired during pretraining, yet this knowledge is inherently static and may become inaccurate or outdated, leading to knowledge hallucinations. Knowledge editing offers an efficient alternative to full retraining by enabling targeted factual updates while preserving overall model behavior. Existing locate-then-edit methods, however, rely on fixed layer selection strategies, treating the locating stage as a static design choice and failing to account for the hierarchical and instance-dependent nature of knowledge representation in LLMs. In this paper, we propose FiDAL, a Fisher-driven adaptation-aware locating strategy that dynamically identifies which model components should be edited for a given knowledge update. FiDAL formulates localization as a weight-level decision problem and leverages Fisher Information to select layers that are both influential and sensitive to factual modifications. A lightweight probing stage with low-rank modulation enables efficient localization with minimal overhead. Experiments on standard benchmarks demonstrate that FiDAL consistently improves editing effectiveness and knowledge preservation across multiple editing methods.
%U https://aclanthology.org/2026.acl-long.957/
%P 20902-20913

Markdown (Informal)

[Fisher-Driven Adaptive Locating for Knowledge Editing in Large Language Models](https://aclanthology.org/2026.acl-long.957/) (Xu et al., ACL 2026)

• Fisher-Driven Adaptive Locating for Knowledge Editing in Large Language Models (Xu et al., ACL 2026)

ACL

• Chenghao Xu, Jiexi Yan, Guangtao Lyu, Qi Liu, Muli Yang, and Cheng Deng. 2026. Fisher-Driven Adaptive Locating for Knowledge Editing in Large Language Models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 20902–20913, San Diego, California, United States. Association for Computational Linguistics.