Continual Unlearning for Large Language Models
A framework for ongoing security maintenance of LLMs
This research introduces a novel Optimize-Once-Operate-Online (OOO) framework that enables continual unlearning in large language models without repeated retraining.
- Addresses security concerns by efficiently removing undesired data from LLMs
- Optimizes for continual unlearning requests that emerge in real-world scenarios
- Reduces computational costs while maintaining model performance
- Provides a practical solution for ongoing model security maintenance
As LLMs become more integrated into business operations, this research offers critical capabilities for managing security risks, responding to regulatory requirements, and protecting against data vulnerabilities without the prohibitive costs of full model retraining.