Deep Contrastive Unlearning for Language Models

• URL: http://arxiv.org/abs/2503.14900v1
• Date: Wed, 19 Mar 2025 04:58:45 GMT
• Title: Deep Contrastive Unlearning for Language Models
• Authors: Estrid He, Tabinda Sarwar, Ibrahim Khalil, Xun Yi, Ke Wang,
• Abstract summary: We propose a machine unlearning framework, named Deep Contrastive Unlearning for fine-Tuning (DeepCUT) language models.<n>Our proposed model achieves machine unlearning by directly optimizing the latent space of a model.
• Score: 9.36216515987051
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The past a few years have witnessed the great success of large language models, demonstrating powerful capabilities in comprehending textual data and generating human-like languages. Large language models achieve success by being trained on vast amounts of textual data, including online sources with copyrighted content and user-generated knowledge. However, this comes at a cost: the potential risk of exposing users' privacy and violating copyright protections. Thus, to safeguard individuals' "right to be forgotten", there has been increasing interests in machine unlearning -- the process of removing information carried by particular training samples from a model while not deteriorating its predictive quality. This is a challenging task due to the black-box nature of language models. Most existing studies focus on mitigating the impact of those forgot samples upon a model's outputs, and do not explicitly consider the geometric distributions of samples in the latent space of a model. To address this issue, we propose a machine unlearning framework, named Deep Contrastive Unlearning for fine-Tuning (DeepCUT) language models. Our proposed model achieves machine unlearning by directly optimizing the latent space of a model. Comprehensive experiments on real-world datasets demonstrate the effectiveness and efficiency of DeepCUT with consistent and significant improvement over baseline methods.

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