Hugging Face Trending Papers
Auditable Unlearning for LLMs
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arXiv AI
1d ago
Auditable Unlearning for LLMs
The paper introduces auditability as a fourth evaluation axis for machine unlearning, measuring the separation between forgotten and retained data via a statistic computed by the provider. Experiments on eleven unlearning methods across TOFU, MUSE, and RWKU datasets show none satisfy all four axes, prompting the authors to propose a two‑stage approach: GapWiden, which widens the audit statistic gap using a hinge loss, and GapVeil, an inference‑time rejection sampler that refines responses based on audit statistics. The combined method achieves oracle‑level auditability and leakage control for all tested unlearning techniques, while improving retain performance for most methods.
arXiv Machine Learning
Jun 16
Auditing Machine Unlearning: A Systematic Research on Whether Models Truly Forget
arXiv:2606. 16110v1 Announce Type: new Abstract: Machine unlearning has been extensively studied in response to growing privacy concerns and regulatory requirements.
Hugging Face Trending Papers
Jun 15
Auditing Machine Unlearning: A Systematic Research on Whether Models Truly Forget
Machine unlearning has been extensively studied in response to growing privacy concerns and regulatory requirements. However, auditing whether unlearning algorithms have truly erased the influence of specific data remains an open challenge.
arXiv Machine Learning
Jul 8
Auditing of Unlearning Algorithms
arXiv:2607. 05898v1 Announce Type: new Abstract: Evaluating whether unlearning algorithms truly remove training data influence remains an open challenge.
arXiv AI
Oct 15
GUARD: Guided Unlearning and Retention via Data Attribution for Large Language Models
arXiv:2506.10946v3 Announce Type: replace-cross Abstract: Unlearning large language models (LLMs) is important due to copyright and privacy concerns. A key challenge is unintended forgetting, where r...
arXiv AI
Jul 2
Auditing Forgetting in Limited Memory Language Models
arXiv:2607. 00605v1 Announce Type: cross Abstract: Limited Memory Language Models (LMLMs) externalize factual knowledge to a database to enable deletion-based unlearning without retraining.