This research investigates how different retain sets impact the effectiveness of entity unlearning in large language models, providing critical insights for balancing privacy protection and model performance.

Key Findings:

• The composition of the retain set significantly influences unlearning effectiveness when removing sensitive entity information
• Strategic selection of retain data can improve both forgetting of targeted information and preservation of general model capabilities
• Entity unlearning emerges as a targeted approach to mitigate privacy risks while maintaining overall model utility

Security Implications:
As LLMs increasingly store sensitive information, these findings enable more effective removal of unauthorized data, enhancing privacy protection without compromising model functionality—a critical advancement for deploying AI systems that comply with privacy regulations and ethical standards.

Which Retain Set Matters for LLM Unlearning? A Case Study on Entity Unlearning