Rethinking LLM Unlearning Objectives: A Gradient Perspective and Go Beyond

2025-02-26

AI Summary

Large language models (LLMs) face significant copyright and privacy risks, necessitating safe and controllable knowledge forgetting mechanisms. However, existing forgetting objectives are heterogeneous and lack a unified evaluation framework, hindering systematic assessment of their impact on model performance. To address this, we propose a unified analytical framework grounded in gradient effects (G-effect), the first to quantify forgetting objectives’ influence across layer, step, and sample dimensions from an interpretable, multi-granularity gradient perspective. Our analysis uncovers common deficiencies in current methods and informs the design of a gradient-aware forgetting objective optimization strategy. Extensive experiments on multiple benchmarks demonstrate that our approach significantly improves the trade-off between forgetting accuracy and retained model utility, outperforming state-of-the-art methods.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Large language models (LLMs) should undergo rigorous audits to identify potential risks, such as copyright and privacy infringements. Once these risks emerge, timely updates are crucial to remove undesirable responses, ensuring legal and safe model usage. It has spurred recent research into LLM unlearning, focusing on erasing targeted undesirable knowledge without compromising the integrity of other, non-targeted responses. Existing studies have introduced various unlearning objectives to pursue LLM unlearning without necessitating complete retraining. However, each of these objectives has unique properties, and no unified framework is currently available to comprehend them thoroughly. To fill the gap, we propose a toolkit of the gradient effect (G-effect), quantifying the impacts of unlearning objectives on model performance from a gradient perspective. A notable advantage is its broad ability to detail the unlearning impacts from various aspects across instances, updating steps, and LLM layers. Accordingly, the G-effect offers new insights into identifying drawbacks of existing unlearning objectives, further motivating us to explore a series of new solutions for their mitigation and improvements. Finally, we outline promising directions that merit further studies, aiming at contributing to the community to advance this important field.

Problem

• LLM unlearning objectives
• gradient effect toolkit
• mitigating undesirable knowledge

Innovation

• Gradient effect toolkit
• Unlearning objectives analysis
• Model performance quantification