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Unlearning can inadvertently reinforce biases if demographic requests are imbalanced, but FAUN ensures fairness while effectively removing sensitive data from MLLMs.
Data mixing, especially with instruction-heavy data, emerges as the crucial factor for optimizing VLM training, challenging traditional filtering approaches.
