GRU framework offers a solution to the critical trade-off between removing harmful content from LLMs while preserving general capabilities.

• Analyzes gradients during unlearning to identify and preserve essential knowledge
• Reduces performance degradation that typically occurs during unlearning processes
• Enables more precise removal of privacy and copyright-related responses
• Maintains model functionality while enhancing security and legal compliance

This research is vital for security professionals as it provides a pathway to deploy safer AI systems that can selectively remove harmful content without compromising overall performance—a key requirement for responsible AI deployment.

GRU: Mitigating the Trade-off between Unlearning and Retention for Large Language Models