aiqbench analysis
This paper proposes SHIFT, a method for removing specific training data from large language models. It targets a weakness of likelihood-suppression approaches, whose forgetting depends on how long they are optimized, and it reports closer alignment to a retrained oracle, including under relearning attacks. The results are benchmark-based (TOFU and MUSE under one protocol), so readers should weigh them against how well those benchmarks reflect real deletion requirements.
Unlearning by Extrapolating a Shrink-and-Heal Lookahead
arXiv:2611.00374v1 Announce Type: new Abstract: Unlearning aims to make a large language model behave like one retrained without its forget set. Prevailing likelihood-suppression objectives, however, lack an explicit forgetting target: they continually reward lower forget-set likelihood, making forgetting sensitive to the optimization budget. We address this limitation by estimating the direction toward the oracle without training it. Our key idea is to shrink the fine-tuned model toward its pre-fine-tuning weights and briefly re-learn the retain set, thereby removing the forget-specific contribution; in the linearized regime, this shrink-and-heal lookahead moves the model a fixed fraction of the way to the retrained model, its unique fixed point. Our method, SHIFT, extrapolates the lookahead's per-token changes into distillation targets and iteratively refines them. Evaluated against eleven baselines under a unified OpenUnlearning protocol, SHIFT achieves the highest aggregate score across all TOFU settings and the closest oracle alignment, 3.7x closer on average than the next-best method; on MUSE, it preserves the most retain knowledge. Under larger optimization budgets, baselines overshoot the oracle, while SHIFT remains near it, even under relearning attacks.
Abstract:Unlearning aims to make a large language model behave like one retrained without its forget set. Prevailing likelihood-suppression objectives, however, lack an explicit forgetting target: they continually reward lower forget-set likelihood, making forgetting sensitive to the optimization budget. We address this limitation by estimating the direction toward the oracle without training it. Our key idea is to shrink the fine-tuned model toward its pre-fine-tuning weights and briefly re-learn the retain set, thereby removing the forget-specific contribution; in the linearized regime, this shrink-and-heal lookahead moves the model a fixed fraction of the way to the retrained model, its unique fixed point. Our method, SHIFT, extrapolates the lookahead's per-token changes into distillation targets and iteratively refines them. Evaluated against eleven baselines under a unified OpenUnlearning protocol, SHIFT achieves the highest aggregate score across all TOFU settings and the closest oracle alignment, 3.7x closer on average than the next-best method; on MUSE, it preserves the most retain knowledge. Under larger optimization budgets, baselines overshoot the oracle, while SHIFT remains near it, even under relearning attacks.
Machine Learning (cs.LG); Computation and Language (cs.CL)
arXiv:2611.00374 [cs.LG]
(or arXiv:2611.00374v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2611.00374 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 270 words