Evaluate the effectiveness of machine unlearning in large language models

Determine a rigorous, standardized methodology to evaluate the effectiveness of machine unlearning in large language models, including clear criteria and metrics for assessing whether targeted knowledge has been removed rather than merely suppressed.

Background

The paper introduces the Stimulus-Knowledge Entanglement-Behavior (SKeB) framework to paper how persuasive prompting and knowledge entanglement influence residual recall in unlearned LLMs. Despite proposing SKeB, the authors explicitly note that reliably evaluating whether unlearning has truly removed specific information remains unresolved.

This open problem is foundational to privacy, safety, and compliance claims around unlearning, since current approaches may suppress direct recall while leaving indirect retrieval pathways intact via framing or entanglement.