Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks

This survey examines multimodal unlearning—methods for removing or forgetting specific data, concepts, or behaviors from AI models trained on vision, language, video, and audio data. The paper catalogs existing unlearning techniques, datasets, and benchmarks, addressing growing enterprise needs around data privacy, regulatory compliance (GDPR, right-to-be-forgotten), and model safety in production systems.

This survey examines multimodal unlearning—methods for removing or forgetting specific data, concepts, or behaviors from AI models trained on vision, language, video, and audio data. The paper catalogs existing unlearning techniques, datasets, and benchmarks, addressing growing enterprise needs around data privacy, regulatory compliance (GDPR, right-to-be-forgotten), and model safety in production systems.

Legal AI must preserve privilege, satisfy ethics rules, and keep client data within a defensible perimeter. QuettaMinds helps law firms and legal departments deploy AI that is structurally compliant, not just policy-compliant.