SIEVE: Selective attention-value Suppression for Vision-Language Models Unlearning

Si Qi Goh; Cap Dang Xuan Kiet; Tat-Jen Cham; Kwok-Yan Lam

cs.LGOct 1, 2026

SIEVE: Selective attention-value Suppression for Vision-Language Models Unlearning

Authors: Si Qi Goh, Cap Dang Xuan Kiet, Tat-Jen Cham, Kwok-Yan Lam

Organizations: Digital Trust Centre, Nanyang Technological University, Singapore · College of Computing and Data Science, Nanyang Technological University, Singapore

Abstract

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.

Figures & tables

Figure 1: The overview of the unlearning settings in VLMs.

Figure 2: The unlearning framework, SIEVE, from our proposed method, shows how both the forgetting and retaining process work within the attention layers.

	Forget $\downarrow$			Retain $\uparrow$			Real $\uparrow$			Test $\downarrow$
Methods	Class	Gen	Cloze	Class	Gen	Cloze	Class	Gen	Cloze	Class	Gen	Cloze
LLaVA-1.5-7B Visual-QA
Vanilla	69.67	0.552	28.18	43.75	0.394	29.59	46.21	0.226	7.19	50.16	0.369	21.33
GA Diff	45.45	0.387	20.78	26.35	0.336	12.60	22.30	0.136	0.60	30.16	0.309	14.00
KL Min	63.25	0.541	18.90	40.51	0.394	22.44	37.46	0.226	8.82	48.52	0.292	1 1.67
NPO	55.68	0.496	27.48	38.14	0.382	18.11	37.80	0.218	6.54	45.41	0.268	20.67

Table 1: Comparison of SIEVE and baseline methods using LLaVA-1.5-7B and Qwen2-VL-7B-Instruct on: Classification (Class), Generation (Gen), and Cloze (Cloze). Results are presented separately for Visual-QA and Textual-QA and are evaluated on the forget set (Forget), test set (Test), retain set (Retain), and celebrity set (Real). Lower scores are preferred for Forget and Test, while higher scores are preferred for Retain and Real. The best and second best are highlighted in bold and underlined , respectively.

Figure 3: Forgetting–utility trade-off between unlearning effectiveness and model utility at 5% and 10% forget ratios on LLaVA-1.5-7B. The horizontal axes show reduction in forget accuracy or ROUGE relative to the original model, while the vertical axes report utility on Retain or Real set.

Figure 4: Case study on Forget and Retain sets before and after unlearning. We report the result of Visual QA for the same ID. Incorrect responses are shown in Red , while correct responses are highlighted in Green .

	Forget $\downarrow$			Retain $\uparrow$			Real $\uparrow$			Test $\downarrow$
Methods	Class	Gen	Cloze	Class	Gen	Cloze	Class	Gen	Cloze	Class	Gen	Cloze
LLaVA-1.5-7B Visual-QA
Vanilla	69.67	0.552	28.18	43.75	0.394	29.59	46.21	0.226	7.19	50.16	0.369	21.33
w/o $\mathcal{L}_{\mathrm{erase}}$	57.42	0.551	26.81	40.25	0.431	17.72	34.84	0.300	8.93	40.33	0.325	10.00
w/o $\mathcal{L}_{\mathrm{preserve}}$	15.25	0.259	13.81	18.45	0.213	8.27	10.10	0.228	0.60	14.10	0.180	17.33
w/o $\mathcal{L}_{\mathrm{forget}}$	64.38	0.573	20.64	39.06	0.422	29.53	41.29	0.273	7.14	46.23	0.299	9.33

Table 2: Ablation study using LLaVA-1.5-7B model. Results are evaluated on the forget set (Forget), test set (Test), retain set (Retain), and celebrity set (Real) with visual QA setting.

Appendix figures & tables20 assets

Supplementary material from the paper’s appendix.

Appendix

Setting	Configuration
Adapted modules	All decoder value projections
LoRA rank $r$ / scaling $\alpha$	$16$ / $16$
LoRA dropout	$0.05$
$(\lambda_{F},\lambda_{E},\lambda_{R},\lambda_{P})$	$(1,1,1,1)$
$\mathcal{H}$	All transformer layers
Optimizer	AdamW

Appendix

Table 3: Implementation’s details in the amended trainer.

Statistics	Number
Total Questions	20,754
* Image + Text Questions	10,377
* Pure Text Questions	10,377
Total Images	1,153
Forget Percentile	5%/10%/15%
Multiple-choice Questions	11,530

Appendix

Table 4: MLLMU-Bench statistics.

Split	# Samples	Attributes
Forget	3000	Birthplace, Occupation, DoB, Annual Salary, Current Residence, Medical Information.
Retain	5197	Name, Gender, Height, Education, Father’s Occupation, Father’s DOB, Mother’s Occupation, Mother’s DOB, Hobby, Food, Pets.

Appendix

Table 5: Attribute partition used for unlearning training.

Group	Input	Evaluation role	# Samples
$\mathcal{E}_{F}^{\mathrm{V}}$	Image + question	Forget	764
$\mathcal{E}_{F}^{\mathrm{T}}$	Text question only	Forget	488
$\mathcal{E}_{R}^{\mathrm{V}}$	Image + question	Retain	236
$\mathcal{E}_{R}^{\mathrm{T}}$	Text question only	Retain	512
$\mathcal{E}_{T}^{\mathrm{V}}$	Image + question	Test	1024
$\mathcal{E}_{T}^{\mathrm{T}}$	Text question only	Test	490

Appendix

Table 6: Dataset groups used during evaluation. The forget/retain assignment is defined before modality grouping.

Figure 5: Additional Visual-QA examples from the Forget and Retain sets. Incorrect responses are shown in Red , while responses that are correct or partially incorrect are highlighted in Green or Yellow accordingly.

Figure 6: Additional Textual-QA examples from the Forget and Retain sets.

Figure 7: Examples from the Real set on real celebrity images under Visual-QA and Textual-QA.

Figure 8: Toy example of key and value suppression.

Figure 9: Case study of key and value suppression before and after manual drawn attention map attack.

	Forget $\downarrow$			Retain $\uparrow$			Real $\uparrow$			Test $\downarrow$
Methods	Class	Gen	Cloze	Class	Gen	Cloze	Class	Gen	Cloze	Class	Gen	Cloze
LLaVA-1.5-7B Visual-QA
Vanilla	69.67	0.552	28.18	43.75	0.394	29.59	46.21	0.226	7.19	50.16	0.369	21.33
Key Suppression	21.13	0.429	9.12	34.32	0.303	5.51	38.01	0.200	8.60	49.28	0.318	21.32
Value Suppression (Ours)	20.06	0.430	15.55	39.20	0.391	27.95	43.90	0.265	8.33	44.43	0.298	9.67

Appendix

Table 7: Results of key suppression and value suppression on both LLaVA-1.5-7B under Visual-QA.

	Forget $\downarrow$			Retain $\uparrow$			Real $\uparrow$			Test $\downarrow$
Methods	Class	Gen	Cloze	Class	Gen	Cloze	Class	Gen	Cloze	Class	Gen	Cloze
LLAVA-1.5-13B Visual-QA
Vanilla	62.82	0.612	32.20	55.44	0.693	34.72	50.78	0.394	19.29	49.51	0.450	27.67
GA Diff	28.56	0 .503	8 .45	2 8.06	0.508	5.91	49.26	0.306	14.26	3 5.90	0 .383	1 2.67
KL Min	2 9.27	0.520	21.07	27.60	0.693	2 5.12	4 9.82	0 .333	1 4.29	43.28	0.402	16.00
Ours	31.53	0.437	5.76	31.03	0 .690	34.72	49.83	0.377	14.88	35.08	0.381	2.33

Appendix

Table 8: Comparison of SIEVE and baseline methods using LLaVA-1.5-13B on: Classification (Class), Generation (Gen), and Cloze (Cloze). Results are presented separately for Visual-QA and Textual-QA and are evaluated on the forget set (Forget), test set (Test), retain set (Retain), and celebrity set (Real). Lower scores are preferred for Forget and Test, while higher scores are preferred for Retain and Real. The best and second best are highlighted in bold and underlined , respectively.

Model	Precision	GPU Memory Usage	Computation Time
LLaVA-1.5-7B	torch.float16	59.4 ( $\pm$ 0.15) GB	1.47 ( $\pm$ 0.06) s/it
LLaVA-1.5-13B	torch.float16	76.6 ( $\pm$ 0.23) GB	3.47 ( $\pm$ 0.04) s/it
Qwen2-VL-7B-Instruct	torch.float16	67.6 ( $\pm$ 0.17) GB	1.22 ( $\pm$ 0.03) s/it

Appendix

Table 9: Empirical study on complexity of different VLMs during training with a single A100.

Figure 10: Total training time of different unlearning methods on LLaVA-1.5-7B with a single A100.

Figure 11: Value of forget $V_{\phi}$ from the vanilla model (top) and ours (bottom) at the last layer.

Figure 12: Value of retain $V_{\phi}$ from the vanilla model (top) and ours (bottom) at the last layer.

Figure 13: Value of forget $V_{\phi}$ from the vanilla model (top) and ours (bottom) at the penultimate layer.

Figure 14: Value of retain $V_{\phi}$ from the vanilla model (top) and ours (bottom) at the penultimate layer.

Figure 15: Effect of the attention-value

V

on LLaVA-1.5-7B. The left and right columns show Visual-QA and Textual-QA results. Each row reports Forget, Retain, and Real performance, with bars for

V\in\{1,0.5,0\}

grouped by classification accuracy (Class), generation ROUGE-L (Gen), and cloze accuracy (Cloze). ROUGE-L scores are multiplied by 100 for the shared axis. Arrows indicate the preferred performance for evaluation.

Figure 16: Value of $P_{\phi}$ from the vanilla model and ours at the last layer.

Figure 17: Value of $P_{\phi}$ from the vanilla model and ours at the penultimate layer.

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Machine unlearning in Vision-Language Models (VLMs) is typically performed at the image or instance level, making it difficult to precisely remove target knowledge without affecting unrelated semantics. This issue is especially pronounced since a single image often contains multiple entangled concepts, including both target concepts to be forgotten and contextual information that should be preserved. In this paper, we propose an interpretable concept-level unlearning framework for VLMs, which constructs a compact task-specific concept vocabulary from the forgetting set using a multimodal large language model. In addition to modality alignment, visual representations are decomposed into sparse, nonnegative combinations of semantic concepts, providing an explicit interface for fine-grained knowledge manipulation. Based on this decomposition, our method formulates unlearning as concept-level optimization, where target concepts are selectively suppressed while intra-instance non-target semantics and global cross-modal knowledge are preserved. Extensive experiments across both in-domain and out-of-domain forgetting settings demonstrate that our method enables more comprehensive target forgetting, better preserves non-target knowledge within the same image, and maintains competitive model utility compared with existing VLM unlearning methods.

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