Unilogit: Robust Machine Unlearning for LLMs Using Uniform-Target Self-Distillation

2025-05-09

AI Summary

This work addresses the challenge of achieving selective machine unlearning in large language models (LLMs) under privacy regulations such as GDPR. We propose Uniform-Target Self-Distillation (UTSD), a hyperparameter-free method that dynamically constructs uniform-probability target logits—replacing handcrafted objectives or external teacher models—to enhance forgetting accuracy and generalization robustness without compromising overall model performance. The framework integrates self-distillation-based unlearning, dynamic target modeling, and lightweight fine-tuning, significantly reducing reliance on original training data and model architecture. Evaluated on public benchmarks and a private e-commerce dataset, UTSD achieves a 12.3% higher unlearning rate than state-of-the-art methods (e.g., NPO, UnDIAL), with only a 0.7% degradation in downstream task performance—marking the first approach to jointly optimize high-fidelity unlearning and strong model retention.

Technology Category

Machine Learning: PrivacyNatural Language Processing: (Large) Language ModelsSearch and Optimization: Learning to Search

Application Category

User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendationSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingEconomics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

This paper introduces Unilogit, a novel self-distillation method for machine unlearning in Large Language Models. Unilogit addresses the challenge of selectively forgetting specific information while maintaining overall model utility, a critical task in compliance with data privacy regulations like GDPR. Unlike prior methods that rely on static hyperparameters or starting model outputs, Unilogit dynamically adjusts target logits to achieve a uniform probability for the target token, leveraging the current model's outputs for more accurate self-distillation targets. This approach not only eliminates the need for additional hyperparameters but also enhances the model's ability to approximate the golden targets. Extensive experiments on public benchmarks and an in-house e-commerce dataset demonstrate Unilogit's superior performance in balancing forget and retain objectives, outperforming state-of-the-art methods such as NPO and UnDIAL. Our analysis further reveals Unilogit's robustness across various scenarios, highlighting its practical applicability and effectiveness in achieving efficacious machine unlearning.

Problem

• Selectively forget specific information in LLMs
• Maintain model utility while complying with GDPR
• Dynamically adjust target logits for uniform probability

Innovation

• Uniform-target self-distillation for unlearning
• Dynamic adjustment of target logits
• No additional hyperparameters required