Rethinking LLM Unlearning Objectives: A Gradient Perspective and Go Beyond

TL;DR

This paper introduces a gradient-based toolkit called G-effect to analyze and improve LLM unlearning objectives, enabling better understanding and mitigation of undesirable knowledge removal without retraining.

Contribution

It proposes a unified gradient perspective toolkit for analyzing LLM unlearning objectives, providing insights and guiding improvements in the field.

Findings

G-effect quantifies unlearning impacts across instances, steps, and layers.

Identifies drawbacks of existing unlearning objectives.

Suggests new directions for enhancing LLM unlearning methods.

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.