WIN-U: Woodbury-Informed Newton-Unlearning as a retain-free Machine Unlearning Framework

Training machine learning models requires the storage of large datasets, which often contain sensitive or private data. Storing data is associated with a number of potential risks which increase over time, such as database breaches and malicious adversaries. Machine unlearning is the study of methods to efficiently remove the influence of training data subsets from previously-trained models. Existing unlearning methods typically require direct access to the "forget set" -- the data to be forgotten-and organisations must retain this data for unlearning rather than deleting it immediately upon request, increasing risks associated with the forget set. We introduce partially-blind unlearning -- utilizing auxiliary information to unlearn without explicit access to the forget set. We also propose a practical framework Reload, a partially-blind method based on gradient optimization and structured weight sparsification to operationalize partially-blind unlearning. We show that Reload efficiently unlearns, approximating models retrained from scratch, and outperforms several forget set-dependent approaches. On language models, Reload unlearns entities using <0.025% of the retain set and <7% of model weights in <8 minutes on Llama2-7B. In the corrective case, Reload achieves unlearning even when only 10% of corrupted data is identified.

Unlearning large language models (LLMs) is important due to copyright and privacy concerns. A key challenge is unintended forgetting, where removing data inadvertently impairs model utility. While prior work has focused on architectural innovations, the influence of data-level factors remains underexplored. We introduce GUARD, a framework for Guided Unlearning And Retention via Data Attribution. GUARD uses a lightweight proxy data attribution metric tailored to LLM unlearning, which efficiently quantifies the "alignment" between the Forget and Retain (or synthetic supplement) Sets. This metric leads to a new unlearning objective that assigns adaptive, nonuniform weights to samples, decaying exponentially with their proxy attribution scores, and thereby provably mitigates unintended retention loss. On the TOFU benchmark with a 10% forget set, applying GUARD to Gradient Ascent (GA) improves Retain Set ROUGE-L from 0.18 to 22.40 (a 124x increase) and Retain Probability from 0.06% to 17.50%, a substantial recovery from the GA collapse. Furthermore, GUARD combined with NPO (Negative Preference Optimization) outperforms Representation Misdirection for Unlearning (RMU) with 33.80% and 53.10% reductions in the Retain Set Sacrifice Rates computed from ROUGE-L and Probability ($ρ_r$ and $ρ_p$), and a 16.01% improvement in knowledge retention on the MUSE NEWS Retain Set. These findings persist when attribution scores are computed using synthetic or highly subsampled (20%) Retain Sets, where NPO+GUARD still reduces $ρ_r$ and $ρ_p$ by up to 43.89% and 48.13% over NPO. Similar improvements are observed on MUSE, with a tolerable absolute privacy leakage degradation of 13.7 compared to the best-performing existing method in the privacy category.

Machine unlearning for large language models (LLMs) often assumes that a pre-defined forget set matches what the model has memorized, but this frequently breaks in realistic privacy settings where the original training data is inaccessible. We term this gap forget-set misalignment and identify two cases. In Under Unlearning, the forget set omits memorized information and leakage persists. In Out-of-Knowledge Unlearning, the algorithm is driven to "forget" knowledge the model never learned, perturbing parameters and degrading utility. Using gradient-level analysis, we show these behaviors arise from misaligned unlearning targets rather than specific optimization choices. We then propose CONfession-to-Forget-Set (CONFS), a data-blind framework that constructs model-aligned forget sets by eliciting and formalizing the model's memorized knowledge. Across synthetic, multimodal, and real-world benchmarks, CONFS approaches Gold-standard performance on several metrics and achieves a competitive forgetting-utility balance, while preserving utility better than other data-blind forget-set constructions.