UIPE introduces a novel approach to completely remove harmful information from Large Language Models by addressing both target data and related knowledge.

• Identifies crucial gaps in existing unlearning methods that focus only on target data
• Demonstrates how related knowledge enables models to reconstruct harmful content
• Provides a comprehensive framework that eliminates both direct and indirect access paths to harmful information
• Achieves more thorough unlearning while preserving model performance on unrelated tasks

This research is critical for AI security as it prevents LLMs from generating harmful content even when prompted indirectly, addressing a significant vulnerability in current AI safety measures.

UIPE: Enhancing LLM Unlearning by Removing Knowledge Related to Forgetting Targets