OBLIVIATE: Robust and Practical Machine Unlearning for Large Language Models

• URL: http://arxiv.org/abs/2505.04416v1
• Date: Wed, 07 May 2025 13:51:42 GMT
• Title: OBLIVIATE: Robust and Practical Machine Unlearning for Large Language Models
• Authors: Xiaoyu Xu, Minxin Du, Qingqing Ye, Haibo Hu,
• Abstract summary: Large language models (LLMs) trained over extensive corpora risk memorizing sensitive, copyrighted, or toxic content.<n>We propose OBLIVIATE, a robust unlearning framework that removes targeted data while preserving model utility.<n>We conduct experiments on multiple datasets, including the Harry Potter series, WMDP, and TOFU.
• Score: 12.848214683467297
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) trained over extensive corpora risk memorizing sensitive, copyrighted, or toxic content. To address this, we propose OBLIVIATE, a robust unlearning framework that removes targeted data while preserving model utility. The framework follows a structured process: extracting target tokens, building retain sets, and fine-tuning with a tailored loss function comprising three components -- masking, distillation, and world fact. Using low-rank adapters (LoRA), it ensures efficiency without compromising unlearning quality. We conduct experiments on multiple datasets, including the Harry Potter series, WMDP, and TOFU, using a comprehensive suite of metrics: forget quality (new document-level memorization score), model utility, and fluency. Results demonstrate its effectiveness in resisting membership inference attacks, minimizing the impact on retained data, and maintaining robustness across diverse scenarios.

Related papers

• GUARD: Generation-time LLM Unlearning via Adaptive Restriction and Detection [36.38245533018162]
  Large Language Models (LLMs) have demonstrated strong capabilities in memorizing vast amounts of knowledge across diverse domains.<n>Existing unlearning efforts typically fine-tune the model with resources such as forget data, retain data, and a calibration model.<n>We propose Generation-time Unlearning via Adaptive Restriction and Detection (GUARD), a framework that enables dynamic unlearning during LLM generation.
  arXiv  Detail & Related papers  (2025-05-19T16:26:58Z)
• Tokens for Learning, Tokens for Unlearning: Mitigating Membership Inference Attacks in Large Language Models via Dual-Purpose Training [13.680205342714412]
  Large language models (LLMs) have become the backbone of modern natural language processing but pose privacy concerns about leaking sensitive training data.<n>We propose a lightweight yet effective empirical privacy defense for protecting training data of language modeling by leveraging the token-specific characteristics.
  arXiv  Detail & Related papers  (2025-02-27T03:37:45Z)
• CLIPErase: Efficient Unlearning of Visual-Textual Associations in CLIP [56.199779065855004]
  We introduce CLIPErase, a novel approach that disentangles and selectively forgets both visual and textual associations. Experiments on the CIFAR-100 and Flickr30K datasets demonstrate that CLIPErase effectively forgets designated associations in zero-shot tasks for multimodal samples.
  arXiv  Detail & Related papers  (2024-10-30T17:51:31Z)
• Silver Linings in the Shadows: Harnessing Membership Inference for Machine Unlearning [7.557226714828334]
  We present a novel unlearning mechanism designed to remove the impact of specific data samples from a neural network. In achieving this goal, we crafted a novel loss function tailored to eliminate privacy-sensitive information from weights and activation values of the target model. Our results showcase the superior performance of our approach in terms of unlearning efficacy and latency as well as the fidelity of the primary task.
  arXiv  Detail & Related papers  (2024-07-01T00:20:26Z)
• The Frontier of Data Erasure: Machine Unlearning for Large Language Models [56.26002631481726]
  Large Language Models (LLMs) are foundational to AI advancements. LLMs pose risks by potentially memorizing and disseminating sensitive, biased, or copyrighted information. Machine unlearning emerges as a cutting-edge solution to mitigate these concerns.
  arXiv  Detail & Related papers  (2024-03-23T09:26:15Z)
• DUEL: Duplicate Elimination on Active Memory for Self-Supervised Class-Imbalanced Learning [19.717868805172323]
  We propose an active data filtering process during self-supervised pre-training in our novel framework, Duplicate Elimination (DUEL) This framework integrates an active memory inspired by human working memory and introduces distinctiveness information, which measures the diversity of the data in the memory. The DUEL policy, which replaces the most duplicated data with new samples, aims to enhance the distinctiveness information in the memory and thereby mitigate class imbalances.
  arXiv  Detail & Related papers  (2024-02-14T06:09:36Z)
• TOFU: A Task of Fictitious Unlearning for LLMs [99.92305790945507]
  Large language models trained on massive corpora of data from the web can reproduce sensitive or private data raising both legal and ethical concerns. Unlearning, or tuning models to forget information present in their training data, provides us with a way to protect private data after training. We present TOFU, a benchmark aimed at helping deepen our understanding of unlearning.
  arXiv  Detail & Related papers  (2024-01-11T18:57:12Z)
• Task-Distributionally Robust Data-Free Meta-Learning [99.56612787882334]
  Data-Free Meta-Learning (DFML) aims to efficiently learn new tasks by leveraging multiple pre-trained models without requiring their original training data. For the first time, we reveal two major challenges hindering their practical deployments: Task-Distribution Shift ( TDS) and Task-Distribution Corruption (TDC)
  arXiv  Detail & Related papers  (2023-11-23T15:46:54Z)
• Unlearn What You Want to Forget: Efficient Unlearning for LLMs [92.51670143929056]
  Large language models (LLMs) have achieved significant progress from pre-training on and memorizing a wide range of textual data. This process might suffer from privacy issues and violations of data protection regulations. We propose an efficient unlearning framework that could efficiently update LLMs without having to retrain the whole model after data removals.
  arXiv  Detail & Related papers  (2023-10-31T03:35:59Z)
• 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.