Year: 2026
Type: conference-paper
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factual knowledge acquired during pretraining, yet this knowledge is inherently static and may become inaccurate or outdated, leading to knowledge hallucinations.Knowledge editing offers an efficient alternative to full retraining by enabling targeted factual updates while preserving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 instancedependent 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.
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G7994072965, G986958842
OpenAlex ID: W7166888331