Rethinking Post-Unlearning Behavior of Large Vision-Language Models

• URL: http://arxiv.org/abs/2506.02541v1
• Date: Tue, 03 Jun 2025 07:28:22 GMT
• Title: Rethinking Post-Unlearning Behavior of Large Vision-Language Models
• Authors: Minsung Kim, Nakyeong Yang, Kyomin Jung,
• Abstract summary: We introduce a new unlearning task for Large Vision-Language Models (LVLMs)<n>This task requires models to provide privacy-preserving yet informative and visually grounded responses.<n>We also propose, a novel unlearning method that explicitly guides post-unlearning behavior toward a desirable output distribution.
• Score: 17.951441278605966
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine unlearning is used to mitigate the privacy risks of Large Vision-Language Models (LVLMs) arising from training on large-scale web data. However, existing unlearning methods often fail to carefully select substitute outputs for forget targets, resulting in Unlearning Aftermaths-undesirable behaviors such as degenerate, hallucinated, or excessively refused responses. We highlight that, especially for generative LVLMs, it is crucial to consider the quality and informativeness of post-unlearning responses rather than relying solely on naive suppression. To address this, we introduce a new unlearning task for LVLMs that requires models to provide privacy-preserving yet informative and visually grounded responses. We also propose PUBG, a novel unlearning method that explicitly guides post-unlearning behavior toward a desirable output distribution. Experiments show that, while existing methods suffer from Unlearning Aftermaths despite successfully preventing privacy violations, PUBG effectively mitigates these issues, generating visually grounded and informative responses without privacy leakage for forgotten targets.

Related papers

• Shadow Unlearning: A Neuro-Semantic Approach to Fidelity-Preserving Faceless Forgetting in LLMs [10.135445130232265]
  We propose Shadow Unlearning, a novel paradigm of approximate unlearning, that performs machine unlearning on anonymized forget data without exposing PII.<n>We further propose a novel privacy-preserving framework, Neuro-Semantic Projector Unlearning (NSPU) to achieve Shadow unlearning.<n> Experimental results show that NSPU achieves superior unlearning performance, preserves model utility, and enhances user privacy.
  arXiv  Detail & Related papers  (2026-01-07T12:11:25Z)
• Erase or Hide? Suppressing Spurious Unlearning Neurons for Robust Unlearning [31.829376135133554]
  Large language models trained on web-scale data can memorize private or sensitive knowledge, raising significant privacy risks.<n>Some unlearning methods remain vulnerable to "relearning" during subsequent training, allowing a substantial portion of forgotten knowledge to resurface.<n>We introduce Ssiuu, a new class of unlearning methods that employs attribution-guided regularization to prevent spurious negative influence and faithfully remove target knowledge.
  arXiv  Detail & Related papers  (2025-09-26T12:27:23Z)
• Keeping an Eye on LLM Unlearning: The Hidden Risk and Remedy [36.19634262653306]
  This paper reveals a critical vulnerability in fine-tuning-based unlearning.<n>A malicious user can craft a manipulated forgetting request that stealthily degrades the model's utility for benign users.<n>We propose Scope-aware Unlearning (SU), a lightweight enhancement that introduces a scope term into the unlearning objective.
  arXiv  Detail & Related papers  (2025-05-31T02:57:24Z)
• Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research [186.53450963176968]
  "Machine unlearning" is a proposed solution for mitigating the existence of content in an AI model that is problematic for legal or moral reasons.<n>We provide a framework for ML researchers and policymakers to think rigorously about these challenges.
  arXiv  Detail & Related papers  (2024-12-09T20:18:43Z)
• A Closer Look at Machine Unlearning for Large Language Models [46.245404272612795]
  Large language models (LLMs) may memorize sensitive or copyrighted content, raising privacy and legal concerns.<n>We discuss several issues in machine unlearning for LLMs and provide our insights on possible approaches.
  arXiv  Detail & Related papers  (2024-10-10T16:56:05Z)
• Learn while Unlearn: An Iterative Unlearning Framework for Generative Language Models [52.03511469562013]
  We introduce the Iterative Contrastive Unlearning (ICU) framework, which consists of three core components.<n>A Knowledge Unlearning Induction module targets specific knowledge for removal using an unlearning loss.<n>A Contrastive Learning Enhancement module preserves the model's expressive capabilities against the pure unlearning goal.<n>An Iterative Unlearning Refinement module dynamically adjusts the unlearning process through ongoing evaluation and updates.
  arXiv  Detail & Related papers  (2024-07-25T07:09:35Z)
• UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI [50.61495097098296]
  We revisit the paradigm in which unlearning is used for Large Language Models (LLMs) We introduce a concept of ununlearning, where unlearned knowledge gets reintroduced in-context. We argue that content filtering for impermissible knowledge will be required and even exact unlearning schemes are not enough for effective content regulation.
  arXiv  Detail & Related papers  (2024-06-27T10:24:35Z)
• Unlearning or Obfuscating? Jogging the Memory of Unlearned LLMs via Benign Relearning [37.061187080745654]
  We show that existing approaches for unlearning in LLMs are surprisingly susceptible to a simple set of $textitbenign relearning attacks.<n>With access to only a small and potentially loosely related set of data, we find that we can ''jog'' the memory of unlearned models to reverse the effects of unlearning.
  arXiv  Detail & Related papers  (2024-06-19T09:03:21Z)
• 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)
• UNDIAL: Self-Distillation with Adjusted Logits for Robust Unlearning in Large Language Models [12.45822383965784]
  We introduce UnDIAL (Unlearning via Self-Distillation on Adjusted Logits), a novel and robust unlearning method. Our approach leverages self-distillation to adjust logits and selectively reduce the influence of targeted tokens.
  arXiv  Detail & Related papers  (2024-02-15T16:21:14Z)
• Rethinking Machine Unlearning for Large Language Models [85.92660644100582]
  We explore machine unlearning in the domain of large language models (LLMs)<n>This initiative aims to eliminate undesirable data influence (e.g., sensitive or illegal information) and the associated model capabilities.
  arXiv  Detail & Related papers  (2024-02-13T20:51:58Z)
• Machine Unlearning in Large Language Models [8.14992136443131]
  This paper introduces a novel machine unlearning framework into large language models. Our objectives are to make LLMs not produce harmful, hallucinatory, or privacy-compromising responses. Experimental results show that our approach effectively meets unlearning objectives without substantially compromising model performance.
  arXiv  Detail & Related papers  (2024-02-03T05:14:56Z)
• 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)
• Knowledge Unlearning for Mitigating Privacy Risks in Language Models [31.322818016245087]
  We propose knowledge unlearning as an alternative method to reduce privacy risks for language models. We show that simply applying the unlikelihood training objective to target token sequences is effective at forgetting them. We show that unlearning can give a stronger empirical privacy guarantee in scenarios where the data vulnerable to extraction attacks are known a priori.
  arXiv  Detail & Related papers  (2022-10-04T10:18:11Z)

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.