Improving the Robustness of Representation Misdirection for Large Language Model Unlearning

• URL: http://arxiv.org/abs/2501.19202v2
• Date: Mon, 03 Feb 2025 14:05:36 GMT
• Title: Improving the Robustness of Representation Misdirection for Large Language Model Unlearning
• Authors: Dang Huu-Tien, Hoang Thanh-Tung, Le-Minh Nguyen, Naoya Inoue,
• Abstract summary: Representation Misdirection (RM) and variants are established large language model (LLM) unlearning methods with state-of-the-art performance. We show that RM methods inherently reduce models' robustness, causing them to misbehave even when a single non-adversarial forget-token is in the retain-query. We propose Random Noise Augmentation -- a model and method approach with theoretical guarantees for improving the robustness of RM methods.
• Score: 6.745464488913924
• License:
• Abstract: Representation Misdirection (RM) and variants are established large language model (LLM) unlearning methods with state-of-the-art performance. In this paper, we show that RM methods inherently reduce models' robustness, causing them to misbehave even when a single non-adversarial forget-token is in the retain-query. Toward understanding underlying causes, we reframe the unlearning process as backdoor attacks and defenses: forget-tokens act as backdoor triggers that, when activated in retain-queries, cause disruptions in RM models' behaviors, similar to successful backdoor attacks. To mitigate this vulnerability, we propose Random Noise Augmentation -- a model and method agnostic approach with theoretical guarantees for improving the robustness of RM methods. Extensive experiments demonstrate that RNA significantly improves the robustness of RM models while enhancing the unlearning performances.

Related papers

• Defensive Dual Masking for Robust Adversarial Defense [5.932787778915417]
  This paper introduces the Defensive Dual Masking (DDM) algorithm, a novel approach designed to enhance model robustness against such attacks. DDM utilizes a unique adversarial training strategy where [MASK] tokens are strategically inserted into training samples to prepare the model to handle adversarial perturbations more effectively. During inference, potentially adversarial tokens are dynamically replaced with [MASK] tokens to neutralize potential threats while preserving the core semantics of the input.
  arXiv  Detail & Related papers  (2024-12-10T00:41:25Z)
• Transferable Adversarial Attacks on SAM and Its Downstream Models [87.23908485521439]
  This paper explores the feasibility of adversarial attacking various downstream models fine-tuned from the segment anything model (SAM) To enhance the effectiveness of the adversarial attack towards models fine-tuned on unknown datasets, we propose a universal meta-initialization (UMI) algorithm.
  arXiv  Detail & Related papers  (2024-10-26T15:04:04Z)
• Avoiding mode collapse in diffusion models fine-tuned with reinforcement learning [0.0]
  Fine-tuning foundation models via reinforcement learning (RL) has proven promising for aligning to downstream objectives. We exploit the hierarchical nature of diffusion models (DMs) and train them dynamically at each epoch with a tailored RL method. We show that models trained with HRF achieve better preservation of diversity in downstream tasks, thus enhancing the fine-tuning robustness and at uncompromising mean rewards.
  arXiv  Detail & Related papers  (2024-10-10T19:06:23Z)
• Robust VAEs via Generating Process of Noise Augmented Data [9.366139389037489]
  This paper introduces a novel framework that enhances robustness by regularizing the latent space divergence between original and noise-augmented data. Our empirical evaluations demonstrate that this approach, termed Robust Augmented Variational Auto-ENcoder (RAVEN), yields superior performance in resisting adversarial inputs.
  arXiv  Detail & Related papers  (2024-07-26T09:55:34Z)
• Stable Unlearnable Example: Enhancing the Robustness of Unlearnable Examples via Stable Error-Minimizing Noise [31.586389548657205]
  Unlearnable example is proposed to significantly degrade the generalization performance of models by adding a kind of imperceptible noise to the data. We introduce stable error-minimizing noise (SEM), which trains the defensive noise against random perturbation instead of the time-consuming adversarial perturbation. SEM achieves a new state-of-the-art performance on CIFAR-10, CIFAR-100, and ImageNet Subset.
  arXiv  Detail & Related papers  (2023-11-22T01:43:57Z)
• Learn from the Past: A Proxy Guided Adversarial Defense Framework with Self Distillation Regularization [53.04697800214848]
  Adversarial Training (AT) is pivotal in fortifying the robustness of deep learning models. AT methods, relying on direct iterative updates for target model's defense, frequently encounter obstacles such as unstable training and catastrophic overfitting. We present a general proxy guided defense framework, LAST' (bf Learn from the Pbf ast)
  arXiv  Detail & Related papers  (2023-10-19T13:13:41Z)
• Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method [115.29382166356478]
  We introduce the adversarial retrieval attack (AREA) task. It is meant to trick DR models into retrieving a target document that is outside the initial set of candidate documents retrieved by the DR model. We find that the promising results that have previously been reported on attacking NRMs, do not generalize to DR models. We propose to formalize attacks on DR models as a contrastive learning problem in a multi-view representation space.
  arXiv  Detail & Related papers  (2023-08-19T00:24:59Z)
• Towards Assessing and Characterizing the Semantic Robustness of Face Recognition [55.258476405537344]
  Face Recognition Models (FRMs) based on Deep Neural Networks (DNNs) inherit this vulnerability. We propose a methodology for assessing and characterizing the robustness of FRMs against semantic perturbations to their input.
  arXiv  Detail & Related papers  (2022-02-10T12:22:09Z)
• SafeAMC: Adversarial training for robust modulation recognition models [53.391095789289736]
  In communication systems, there are many tasks, like modulation recognition, which rely on Deep Neural Networks (DNNs) models. These models have been shown to be susceptible to adversarial perturbations, namely imperceptible additive noise crafted to induce misclassification. We propose to use adversarial training, which consists of fine-tuning the model with adversarial perturbations, to increase the robustness of automatic modulation recognition models.
  arXiv  Detail & Related papers  (2021-05-28T11:29:04Z)
• Learning to Generate Noise for Multi-Attack Robustness [126.23656251512762]
  Adversarial learning has emerged as one of the successful techniques to circumvent the susceptibility of existing methods against adversarial perturbations. In safety-critical applications, this makes these methods extraneous as the attacker can adopt diverse adversaries to deceive the system. We propose a novel meta-learning framework that explicitly learns to generate noise to improve the model's robustness against multiple types of attacks.
  arXiv  Detail & Related papers  (2020-06-22T10:44:05Z)
• RAB: Provable Robustness Against Backdoor Attacks [20.702977915926787]
  We focus on certifying the machine learning model robustness against general threat models, especially backdoor attacks. We propose the first robust training process, RAB, to smooth the trained model and certify its robustness against backdoor attacks. We conduct comprehensive experiments for different machine learning (ML) models and provide the first benchmark for certified robustness against backdoor attacks.
  arXiv  Detail & Related papers  (2020-03-19T17:05:51Z)

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.