Multilinguality at the Edge: Developing Language Models for the Global South

2026-04-23

AI Summary

This study addresses the challenges of deploying multilingual language models in the Global South, where high linguistic diversity and constrained computational resources pose significant barriers. Synthesizing insights from a systematic review of 232 publications, this work pioneers an integrative approach that bridges multilingual natural language processing with edge computing. It proposes a co-optimization framework tailored for low-resource, multilingual settings, combining advances in multilingual modeling, edge computing optimization, and low-resource learning techniques. Through cross-stage technical evaluation, the research identifies critical bottlenecks and offers actionable recommendations for researchers, developers, and policymakers to advance equitable and inclusive language technologies.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Language and Vision

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Where and how language models (LMs) are deployed determines who can benefit from them. However, there are several challenges that prevent effective deployment of LMs in non-English-speaking and hardware constrained communities in the Global South. We call this challenge the last mile: the intersection of multilinguality and edge deployment, where the goals are aligned but the technical requirements often compete. Studying these two fields together is both a need, as linguistically diverse communities often face the most severe infrastructure constraints, and an opportunity, as edge and multilingual NLP research remain largely siloed. To understand the state of the art and the challenges of combining the two areas, we survey 232 papers that tackle this problem across the language modelling pipeline, from data collection to development and deployment. We also discuss open questions and provide actionable recommendations for different stakeholders in the NLP ecosystem. Finally, we hope that this work contributes to the development of inclusive and equitable language technologies.

Problem

• multilinguality
• edge deployment
• language models
• Global South
• last mile

Innovation

• multilinguality
• edge deployment
• language models
• Global South
• last mile