How Hard Can It Be? Hardness-Aware Multi-Objective Unlearning

• URL: http://arxiv.org/abs/2606.02119v1
• Date: Mon, 01 Jun 2026 11:49:47 GMT
• Title: How Hard Can It Be? Hardness-Aware Multi-Objective Unlearning
• Abstract summary: Machine unlearning aims to remove the influence of specific forget training data due to privacy, copyright or bias concerns.<n>We derive an unlearning algorithm with the overall goal of guaranteeing a specified improvement in forget quality.<n>Our hardness measure informs users when retain utility degradation is unavoidable.
• Score: 75.97729821186815
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Machine unlearning aims to remove the influence of specific forget training data due to privacy, copyright or bias concerns while maintaining the model performance on the remaining retain data. Existing unlearning algorithms, such as optimizing a weighted combination of losses, have tried to achieve these objectives of improving forget quality and maintaining retain utility. However, they do not guarantee that these objectives can be improved by a specified extent for all forget and retain data. In this work, we address this limitation with a novel and theoretically-grounded approach from a constrained optimization perspective. Firstly, we identify that the hardness of reconciling both objectives can be quantified by the similarity between the forget data and the retain data. Next, we derive an unlearning algorithm (HAMU) with the overall goal of guaranteeing a specified improvement in forget quality while minimizing the retain utility cost/degradation by updating the model weights based on our hardness measure. Our hardness measure also informs users when retain utility degradation is unavoidable, i.e., both objectives cannot be improved simultaneously, and stopping should be considered. Our algorithm is applicable to non-convex models and is easily parallelizable, making it readily deployable in real-world scenarios. We empirically demonstrate HAMU's superior performance over baselines on both image and text datasets using large models. Our code is available at https://github.com/aoi3142/HAMU.

Related papers

• Direct Token Optimization: A Self-contained Approach to Large Language Model Unlearning [9.42887167048224]
  Machine unlearning is an emerging technique that removes the influence of a subset of training data (forget set) from a model without full retraining.<n>The key challenge lies in ensuring that the model completely forgets the knowledge of the forget set without compromising its overall utility.<n>We propose direct token optimization (DTO), a novel self-contained unlearning approach for large language models.
  arXiv  Detail & Related papers  (2025-09-30T18:05:06Z)
• OFMU: Optimization-Driven Framework for Machine Unlearning [5.100622189286672]
  Large language models increasingly require the ability to unlearn specific knowledge, such as user requests, copyrighted materials, or outdated information.<n>We propose OFMU, a penalty-based bi-level optimization framework that explicitly prioritizes forgetting while preserving retention.<n>We show that OFMU consistently outperforms existing unlearning methods in both efficacy and retained utility.
  arXiv  Detail & Related papers  (2025-09-26T15:31:32Z)
• IMU: Influence-guided Machine Unlearning [12.87795856802456]
  Machine unlearning (MU) enables models to selectively forget specific data points upon request.<n>Most existing MU algorithms require partial or full fine-tuning on the retain set.<n>We propose Influence-guided Machine Unlearning (IMU), a simple yet effective method that conducts MU using only the forget set.
  arXiv  Detail & Related papers  (2025-08-03T07:00:28Z)
• BLUR: A Bi-Level Optimization Approach for LLM Unlearning [100.90394814817965]
  We argue that it is important to model the hierarchical structure of the unlearning problem.<n>We propose a novel algorithm, termed Bi-Level UnleaRning (textttBLUR), which delivers superior performance.
  arXiv  Detail & Related papers  (2025-06-09T19:23:05Z)
• Constrained Entropic Unlearning: A Primal-Dual Framework for Large Language Models [14.321060805197874]
  Large Language Models (LLMs) deployed in real-world settings increasingly face the need to unlearn sensitive, outdated, or proprietary information.<n>Existing unlearning methods formulate forgetting and retention as a regularized trade-off, combining both objectives into a single scalarized loss.<n>We propose a new formulation of LLM unlearning as a constrained optimization problem: forgetting is enforced via a novel logit-margin flattening loss.
  arXiv  Detail & Related papers  (2025-06-05T17:55:23Z)
• UniErase: Towards Balanced and Precise Unlearning in Language Models [69.04923022755547]
  Large language models (LLMs) require iterative updates to address the outdated information problem.<n>UniErase is a novel unlearning framework that demonstrates precision and balanced performances between knowledge unlearning and ability retaining.
  arXiv  Detail & Related papers  (2025-05-21T15:53:28Z)
• Pseudo-Probability Unlearning: Towards Efficient and Privacy-Preserving Machine Unlearning [59.29849532966454]
  We propose PseudoProbability Unlearning (PPU), a novel method that enables models to forget data to adhere to privacy-preserving manner. Our method achieves over 20% improvements in forgetting error compared to the state-of-the-art.
  arXiv  Detail & Related papers  (2024-11-04T21:27:06Z)
• MUSE: Machine Unlearning Six-Way Evaluation for Language Models [109.76505405962783]
  Language models (LMs) are trained on vast amounts of text data, which may include private and copyrighted content. We propose MUSE, a comprehensive machine unlearning evaluation benchmark. We benchmark how effectively eight popular unlearning algorithms can unlearn Harry Potter books and news articles.
  arXiv  Detail & Related papers  (2024-07-08T23:47:29Z)
• RelaxLoss: Defending Membership Inference Attacks without Losing Utility [68.48117818874155]
  We propose a novel training framework based on a relaxed loss with a more achievable learning target. RelaxLoss is applicable to any classification model with added benefits of easy implementation and negligible overhead. Our approach consistently outperforms state-of-the-art defense mechanisms in terms of resilience against MIAs.
  arXiv  Detail & Related papers  (2022-07-12T19:34:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.