This research presents a novel framework for targeted knowledge removal in large language models while preserving overall model performance.

• Introduces gradient-based unlearning objectives that selectively erase undesirable information
• Addresses critical copyright and privacy concerns through precise knowledge deletion
• Enables safer LLM deployment by facilitating risk mitigation after model audits
• Preserves model integrity for non-targeted knowledge areas

From a security perspective, this approach allows organizations to quickly respond to identified risks in deployed LLMs, creating more legally compliant and ethically sound AI systems.

Rethinking LLM Unlearning Objectives: A Gradient Perspective and Go Beyond