Papers › Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning

Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning

9 Oct 2024arXiv:2410.07163archive 2025-07-28

Chongyu Fan, Jiancheng Liu, Licong Lin, Jinghan Jia, Ruiqi Zhang, Song Mei, Sijia Liu

In this work, we address the problem of large language model (LLM) unlearning, aiming to remove unwanted data influences and associated model capabilities (e.g., copyrighted data or harmful content generation) while preserving essential model utilities, without the need for retraining from scratch. Despite the growing need for LLM unlearning, a principled optimization framework remains lacking. To this end, we revisit the state-of-the-art approach, negative preference optimization (NPO), and identify the issue of reference model bias, which could undermine NPO's effectiveness, particularly when unlearning forget data of varying difficulty. Given that, we propose a simple yet effective unlearning optimization framework, called SimNPO, showing that 'simplicity' in removing the reliance on a reference model (through the lens of simple preference optimization) benefits unlearning. We also provide deeper insights into SimNPO's advantages, supported by analysis using mixtures of Markov chains. Furthermore, we present extensive experiments validating SimNPO's superiority over existing unlearning baselines in benchmarks like TOFU and MUSE, and robustness against relearning attacks. Codes are available at https://github.com/OPTML-Group/Unlearn-Simple.

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OPTML-Group/Unlearn-Simple officialmentioned in papermentioned on GitHubpytorchMIT report
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collate_fn OPTML-Group/Unlearn-Simple/TOFU/data_module.py official repository ran MIT (permissive) · cdd171f20cd6f53d · report
compute_metrics OPTML-Group/Unlearn-Simple/WMDP/src/unlearn/base.py official repository ran MIT (permissive) · 88a4f13abd54a00c · report
cross_entropy_loss OPTML-Group/Unlearn-Simple/synthetic/unlearn_utils.py official repository ran MIT (permissive) · 04d9dfc17842cf17 · report
find_all_linear_names OPTML-Group/Unlearn-Simple/TOFU/finetune.py official repository ran · our draft was wrong MIT (permissive) · 649fc48067a48529 · report
get_activation OPTML-Group/Unlearn-Simple/synthetic/model.py official repository ran MIT (permissive) · 75df7d35faaa9ff0 · report
get_loss OPTML-Group/Unlearn-Simple/WMDP/src/unlearn/base.py official repository ran MIT (permissive) · 650d5f6bfcd7d9bb · report
grad_ascent_loss OPTML-Group/Unlearn-Simple/synthetic/unlearn_utils.py official repository ran MIT (permissive) · f8e37979490cc066 · report
grad_descent_loss OPTML-Group/Unlearn-Simple/synthetic/unlearn_utils.py official repository ran MIT (permissive) · 3359083515d97775 · report
convert_raw_data_to_model_format OPTML-Group/Unlearn-Simple/TOFU/data_module.py official repository unverified MIT (permissive) · 7dec325d088c0b76 · report
unlearncollector OPTML-Group/Unlearn-Simple/TOFU/data_module.py official repository unverified MIT (permissive) · 52f769b837c6e32a · report

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Language ModelingLanguage ModellingLarge Language Model

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Tofu

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