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MU-Bench: A Multitask Multimodal Benchmark for Machine Unlearning
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Recent advancements in Machine Unlearning (MU) have introduced solutions to selectively remove certain training samples, such as those with outdated or sensitive information, from trained models. Despite these advancements, evaluation of MU methods have been inconsistent, employing different trained models and architectures, and sample removal strategies, which hampers accurate comparison. In addition, prior MU approaches have mainly focused on singular tasks or modalities, which is not comprehensive. To address these limitations, we develop MU-Bench, the first comprehensive benchmark for MU that (i) unifies the sets of deleted samples and trained models, and (ii) provides broad coverage of tasks and data modalities, including previously unexplored domains such as speech and video classification. Our evaluation show that RandLabel and SalUn are the most effective general MU approaches on MU-Bench, and BadT and SCRUB are capable of achieving random performance on the deletion set. We analyze several under-investigated aspects of unlearning, including scalability, the impacts of parameter-efficient fine-tuning and curriculum learning, and susceptibility to dataset biases. MU-Bench provides an easy-to-use package that includes dataset splits, models, and implementations, together with a leader board to enable unified and scalable MU research.
Forward citations
Cited by 8 Pith papers
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ICU-Bench:Benchmarking Continual Unlearning in Multimodal Large Language Models
ICU-Bench is a new continual unlearning benchmark for MLLMs using 1000 privacy profiles, 9500 images, and 100 forget tasks, showing existing methods fail to balance forgetting, utility, and scalability.
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A Model Merging Approach for Continual MLLM Unlearning
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Understanding Machine Unlearning Through the Lens of Mode Connectivity
Unlearned models usually connect to their originals by smooth low-loss paths, and the smoothness of that path can predict how hard the unlearning task was.
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A Mechanistic Perspective and Circuit-Guided Difficulty Metric for Unlearning
A circuit-similarity score predicts which samples an LLM unlearning method will fail to erase, with hard samples relying on deeper, output-facing pathways.
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FUTURE: Flexible Unlearning for Tree Ensemble
FUTURE forgets training samples from tree ensembles by optimizing sigmoid-smoothed split thresholds and copying them back to the original discrete trees.
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Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
A system-first taxonomy and literature synthesis of multimodal unlearning across vision, language, video, and audio, with datasets, benchmarks, metrics, applications, and open challenges.
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A Numerical PDEs Approach to Evolution Equations in Shape Analysis Based on Regularized Morphoelasticity
Regularized morphoelasticity yields a high-order elliptic system for continuous shape evolution that is solved by mixed finite elements in FEniCSx within an LDDMM-style optimal-control growth model.
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Speech Unlearning
Existing machine unlearning methods perform poorly on speech tasks, and a SuperLoss-based structured forgetting strategy improves forgetting but is reported with almost no experimental detail.
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