Forgetting-MarI: LLM Unlearning via Marginal Information Regularization

ICLR 2026 Conference Submission13935 Authors

Keywords: Machine Unlearning, LLM Unlearning, Data Privacy, Information-Theoretic Regularization, Mutual Information

Abstract: As AI models are trained on ever-expanding datasets, the ability to remove the influence of specific data from trained models has become essential for privacy protection and regulatory compliance, such as the ''right to be forgotten." Unlearning addresses this challenge by selectively removing parametric knowledge from the trained models without retraining from scratch, which is critical for resource-intensive models such as Large Language Models (LLMs). Existing unlearning methods often degrade model performance by removing more information than necessary when attempting to ''forget" specific data. We introduce $\textit{Forgetting-MarI}$, an LLM unlearning framework that provably removes only the additional (marginal) information contributed by the data to be unlearned, while preserving the information supported by the data to be retained. By penalizing marginal information, our method yields an explicit upper bound on the unlearn dataset’s residual influence in the trained models, providing provable undetectability. Extensive experiments confirm that our approach outperforms current state-of-the-art unlearning methods, delivering reliable forgetting and better preserved general model performance across diverse benchmarks. This advancement represents an important step toward making AI systems more controllable and compliant with privacy and copyright regulations without compromising their effectiveness.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

Submission Number: 13935