Core Applications and Techniques of RAG for Low-Resource Languages

Abstract

Low-resource languages (LRLs) are spoken by billions of people globally, yet they continue to face limited access to reliable AI-driven tools, largely due to a lack of sufficient digital text resources. This review examines recent progress in Retrieval-Augmented Generation (RAG) that addresses the specific difficulties encountered in processing LRLs. By analyzing key studies, a range of optimization strategies are identified and analyzed across the RAG workflow—including data handling, retrieval techniques, and generation refinements. The discussion emphasizes how these methods tackle fundamental problems such as scarce data, weak model performance, and poor cultural alignment. The role of cross-lingual retrieval and knowledge distillation is also explored as a way to make AI systems more accessible and useful for speakers of low-resource languages. In addition to outlining RAG’s potential for reducing language-based digital inequality, this survey notes remaining obstacles and suggests productive avenues for further research. It is hoped that this structured summary of methods and use cases will support future efforts toward inclusive AI and the protection of linguistic diversity.