MLLMEraser is a training‑free, input‑aware framework that achieves test‑time unlearning for multimodal large language models by steering activations. It constructs an erasure direction through adversarial contrast between knowledge‑recall and knowledge‑erasure image‑text pairs, and applies this direction selectively to preserve retained knowledge while forgetting specified content. Experiments on LLaVA‑1.5 and Qwen‑2.5‑VL show that MLLMEraser outperforms existing unlearning baselines with lower computational cost and minimal utility loss.
arXiv:2511. 20196v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) can inadvertently memorize privacy-sensitive information during training.
arXiv:2607. 18615v1 Announce Type: cross Abstract: Machine unlearning for vision-language models (VLMs) remains underexplored.
The paper discusses the rapid progress of machine unlearning for multimodal large language models (MLLMs) and the need for rigorous evaluation. It identifies three pitfalls in current benchmarks: lack of held‑out evaluation, reliance on synthetic fine‑tuned knowledge, and unfair comparisons across methods. To address these issues, the authors propose GEMMU, a benchmark using real‑world knowledge and a principled protocol, and show that re‑evaluating existing methods under GEMMU uncovers significant generalization gaps.
Machine unlearning for vision-language models (VLMs) remains underexplored. Unlike language models, VLMs combine a language backbone with visual components, which makes unlearning more complex.
arXiv:2608. 03791v1 Announce Type: new Abstract: Vision-Language Models (VLMs), like Large Language Models (LLMs), may memorize sensitive, copyrighted, or harmful knowledge from their pretraining corpora.
arXiv:2608.01008v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) can memorize and disclose sensitive information, motivating machine unlearning to remove designated knowle...
arXiv:2607. 12112v1 Announce Type: cross Abstract: Federated fine-tuning of Multimodal Large Language Models (MLLMs) across distributed networks enables privacy-sensitive adaptation to evolving data streams, yet a fundamental obstacle prevents robust deployment in dynamic environments: catastrophic forgetting, wherein sequential task updates erase previously acquired knowledge across visual, linguistic, and cross-modal representations.
Vision-Language Models (VLMs), like Large Language Models (LLMs), may memorize sensitive, copyrighted, or harmful knowledge from their pretraining corpora. Removing such knowledge is essential for building trustworthy AI systems.
arXiv:2606. 00105v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress on vision-language tasks, but they may also memorize and expose sensitive or restricted knowledge, raising concerns about privacy and broader safety risks.
arXiv:2606. 12809v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are trained on massive multimodal data, making data unlearning increasingly important as data owners may request the removal of specific content.
arXiv:2605. 20247v2 Announce Type: replace-cross Abstract: Catastrophic forgetting remains a major obstacle to continual learning in large language models (LLMs) and vision--language models (VLMs).
arXiv:2607. 02020v1 Announce Type: new Abstract: Multimodal large language models must continually adapt to evolving tasks and domains, yet standard continual learning metrics mainly measure whether old answers remain correct, leaving the stability of multimodal grounding largely unexamined.