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Evaluating Deep Unlearning in Large Language Models

arXiv — cs.CL•Thursday, November 13, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
The study on deep unlearning in large language models (LLMs) addresses a critical aspect of machine unlearning, which is essential for developing safe and trustworthy AI systems. By focusing on not just removing a specific fact but also preventing its deduction through retained knowledge, the research proposes a novel approach that includes new metrics for measuring unlearning efficacy. Utilizing the MQuAKE dataset for one-step deductions and the newly constructed Eval-DU for multi-step deductions, the experiments reveal that existing methods often fail to achieve deep unlearning or inadvertently remove unrelated facts. This highlights the limitations of current techniques and emphasizes the necessity for targeted algorithms that can robustly handle deep fact unlearning, ensuring that LLMs can be safely and effectively utilized in various applications.
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