EvoEdit: Evolving Null-space Alignment for Robust and Efficient Knowledge Editing
This addresses the need for robust and efficient model editing in dynamically evolving information settings, offering a solution for updating LLMs without full retraining, though it is incremental as it builds on existing locate-then-edit frameworks.
The paper tackles the problem of catastrophic interference in sequential knowledge editing for large language models, where new edits degrade previously integrated updates, and introduces EvoEdit, which achieves better or comparable performance to prior state-of-the-art methods with up to 3.53 times speedup.
Large language models (LLMs) require continual updates to rectify outdated or erroneous knowledge. Model editing has emerged as a compelling paradigm for introducing targeted modifications without the computational burden of full retraining. Existing approaches are mainly based on a locate-then-edit framework. However, in sequential editing contexts, where multiple updates are applied over time, they exhibit significant limitations and suffer from catastrophic interference, i.e., new edits compromise previously integrated updates and degrade preserved knowledge. To address these challenges, we introduce EvoEdit, a novel editing strategy that mitigates catastrophic interference through sequential null-space alignment, enabling stable and efficient model editing. By performing sequential null-space alignment for each incoming edit, EvoEdit preserves both original and previously modified knowledge representations and maintains output invariance on preserved knowledge even across long edit sequences, effectively mitigating interference. Evaluations on real-world sequential knowledge-editing benchmarks show that EvoEdit achieves better or comparable performance than prior state-of-the-art locate-then-edit techniques, with up to 3.53 times speedup. Overall, these results underscore the necessity of developing more principled approaches for designing LLMs in dynamically evolving information settings, while providing a simple yet effective solution with strong theoretical guarantees.