Investigating Model Editing for Unlearning in Large Language Models
Published:Dec 25, 2025 05:00
•1 min read
•ArXiv NLP
Analysis
This paper explores the application of model editing techniques, typically used for modifying model behavior, to the problem of machine unlearning in large language models. It investigates the effectiveness of existing editing algorithms like ROME, IKE, and WISE in removing unwanted information from LLMs without significantly impacting their overall performance. The research highlights that model editing can surpass baseline unlearning methods in certain scenarios, but also acknowledges the challenge of precisely defining the scope of what needs to be unlearned without causing unintended damage to the model's knowledge base. The study contributes to the growing field of machine unlearning by offering a novel approach using model editing techniques.
Key Takeaways
- •Model editing offers a promising alternative to traditional unlearning methods in LLMs.
- •Defining the scope of unlearning remains a significant challenge.
- •Model editing techniques can improve the quality of forgetting in specific scenarios.
Reference
“model editing approaches can exceed baseline unlearning methods in terms of quality of forgetting depending on the setting.”