الملخص
The ability of vision-language models (VLMs) to associate visual identities with biographical information creates a need for selective unlearning of personally identifiable information (PII) while preserving permitted knowledge about the same individual. This setting is challenging because both sensitive and retained information can share the same visual inputs and intermediate representations. We introduce SIEVE, a simple and effective framework for selective VLM unlearning. SIEVE directly regularizes attention-value representations while also controlling model outputs. SIEVE suppresses attention values for forget examples toward a constant zero, while preserving retain-example representations by matching them to a frozen reference model. These objectives are combined with sequence-level forget and retain supervision, enabling targeted forgetting without largely affecting retained knowledge. Extensive experiments show that SIEVE achieves state-of-the-art performance on unlearning with multiple model-modality settings, while maintaining competitive retained utility. Ablation studies further show that value suppression and negative cross-entropy contribute complementary forgetting signals, while reference-based value matching substantially reduces utility degradation. These results demonstrate that attention values provide an effective intervention point for selective multimodal unlearning when sensitive and retained knowledge are closely related.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Goh, S. Q., Kiet, C. D. X., Cham, T. J., & Lam, K. Y. (2026). SIEVE: Selective attention-value Suppression for Vision-Language Models Unlearning. https://omanscience.com/ar/articles/sieve-selective-attention-value-suppression-for-vision-language-models-unlearning
MLA 9
Goh, Si Qi, et al. "SIEVE: Selective attention-value Suppression for Vision-Language Models Unlearning." https://omanscience.com/ar/articles/sieve-selective-attention-value-suppression-for-vision-language-models-unlearning.
شيكاغو (المؤلف–التاريخ)
Goh, Si Qi, Cap Dang Xuan Kiet, Tat-Jen Cham, and Kwok-Yan Lam. 2026. "SIEVE: Selective attention-value Suppression for Vision-Language Models Unlearning." https://omanscience.com/ar/articles/sieve-selective-attention-value-suppression-for-vision-language-models-unlearning.
هارفارد
Goh, S. Q., Kiet, C. D. X., Cham, T. J. and Lam, K. Y. (2026) 'SIEVE: Selective attention-value Suppression for Vision-Language Models Unlearning', Available at: https://omanscience.com/ar/articles/sieve-selective-attention-value-suppression-for-vision-language-models-unlearning.
فانكوفر
Goh SQ, Kiet CDX, Cham TJ, Lam KY. SIEVE: Selective attention-value Suppression for Vision-Language Models Unlearning. https://omanscience.com/ar/articles/sieve-selective-attention-value-suppression-for-vision-language-models-unlearning
IEEE
S. Q. Goh, C. D. X. Kiet, T. J. Cham, and K. Y. Lam, "SIEVE: Selective attention-value Suppression for Vision-Language Models Unlearning," https://omanscience.com/ar/articles/sieve-selective-attention-value-suppression-for-vision-language-models-unlearning.