Cross-Modal Augmentation for Low-Resource Language Understanding and Generation

AbstractThis paper introduces a multimodal retrieval-augmented generation (RAG) system designed to enhance language understanding and generation for low-resource languages. By integrating textual, visual, and geospatial data, the system leverages cross-lingual adaptation and multimodal augmentation to bridge the gap between high-resource and low-resource languages. Evaluated on the MM-COVID and LORELEI datasets, the system demonstrates superior performance in retrieval (precision: 85%, recall: 82%) and generation (BLEU: 28.4) tasks compared to baselines. Case studies in public health communication and disaster response highlight its practical utility. The results underscore the potential of multimodal AI to democratize access to technology and address global challenges in low-resource settings.