Class Machine Unlearning for Complex Data via Concepts Inference and Data Poisoning
The framework addresses difficulty in machine unlearning complex data by explicitly identifying semantic concepts that strongly contribute to a target class or knowledge. It uses these identified concepts to guide model updates via concept-guided poisoning, offering a unified method for image classifiers and LLMs. This approach effectively removes target influence while preserving the model's overall utility.