KAIST introduces balanced multimodal training to reduce modality bias

KAIST researchers developed a novel augmentation technique that forces multimodal models to treat text, images, and audio more equally. By deliberately feeding mismatched data pairs during training, the model learns to avoid over-relying on any single modality. Early experiments show improved accuracy across vision + language benchmarks. The method hopes to alleviate modality bias that often hurts performance in multimodal systems trained on heterogeneous data. The research is shared openly to help the AI community adopt more balanced training approaches.