Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
arXiv:2607.07907v1 Announce Type: cross Abstract: With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associations that originate from their training data. Retraining after deletion requests or policy updates is often impractical, and targeted forgetting remains difficult because knowledge is distributed across shared representations. Multimodal unlearning addresses this challenge by enabling selective removal across modalities while retaining overall utility. This survey offer
The rapid and widespread adoption of multimodal foundation models (VLMs, DMs, LLMs, AFMs) necessitates solutions for managing inadvertently encoded problematic data, as retraining is increasingly impractical.
The ability to selectively 'unlearn' sensitive or biased information across modalities is crucial for mitigating risks, ensuring ethical AI deployment, and complying with future regulations.
This research outlines methods for targeted content removal in multimodal AI, moving beyond costly full model retraining and enabling more dynamic content moderation and ethical safeguards.
- · AI developers
- · Cloud service providers
- · Ethics & compliance professionals
- · Data privacy advocates
- · Companies with poor data governance
- · Malicious actors leveraging AI biases
Introduction of targeted unlearning capabilities into multimodal AI models.
Increased trust in AI systems due to better control over sensitive or unwanted information.
New regulatory frameworks specifically addressing 'right to be forgotten' within complex AI models across different modalities.
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Read at arXiv cs.CL