Work

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

Year: 2025

Type: conference-paper

Abstract: This paper introduces a multimodal retrievalaugmented 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.

Source: Workshop on Multimodal Augmented Generation via Multimodal Retrieval (MAGMaR)

DOI: 10.18653/v1/2025.magmar-1.9

Authors: Zichao Li, Zong Ke

Institution: National University of Singapore

Language: English

Topic: Multimodal Machine Learning Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Reduced inequalities

Open Access status: bronze

OpenAlex ID: W4412944325