cross-modal

MNAFT (Modality Neuron-Aware Fine-Tuning) is a novel approach that selectively updates language-specific and language-agnostic neurons in multimodal large language models to improve image translation while preserving pre-trained knowledge. The method outperforms state-of-the-art image translation techniques including cascaded models and standard fine-tuning approaches.