Multilinguality at the Edge
Developing Language Models for the Global South
Where and how language models are deployed determines who can benefit from them. Communities in the Global South face what we call the last mile: the intersection of multilinguality and edge deployment, where the goals are aligned but the technical requirements often compete. We survey 232 papers that tackle this problem across the language modelling pipeline, from data collection to development and deployment, and provide actionable recommendations for different stakeholders in the NLP ecosystem.
Read the full 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.
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What shapes multilingual edge LM development, and how can these models reach the communities that need them the most?
Communities with the highest linguistic diversity often face the most severe infrastructure constraints. Countries such as Papua New Guinea, Nigeria, and Chad are linguistically diverse yet among the least active on the Internet.
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Answering this requires combining multilinguality and edge NLP. But combining the two areas is hard: while their goals are aligned, their technical requirements often compete, and the two research communities remain largely siloed. The field has several names for this tension: the low-resource double bind (Ahia et al., 2021), the square-one bias (Ruder et al., 2022), Zeno's paradox of language technology (Nigatu et al., 2024), among others.
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.
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The requirements for deploying on the edge and supporting multilinguality often have competing requirements that impose challenges across the language modelling pipeline. Click on each pipeline stage (or requirement) to read about the challenges and the state of the art.
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Analysis
We also looked into edge LM systems, which we define as completed efforts that have been integrated into real-world applications. To identify them, we manually classified each of the 232 papers on whether an actual model deployment took place, obtaining 36 systems in the process.
How are edge LMs developed?
To examine how edge LM systems are made, we situate the 36 deployment papers within the broader 232 surveyed papers. We embed each abstract with MiniLM, reduce to 2D with UMAP, and cluster with HDBSCAN; KeyBERT extracts the top keywords per cluster. Hover any cluster to see representative papers.
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Who develops edge LMs?
We classified the affiliations of authors across the 36 deployment papers into four sectors: Academia (universities and affiliated research institutions), Industry (startups to enterprise), Research collective (non-profit research organizations), and Government (state-affiliated institutes, public sector). Authors with multiple affiliations are counted in each. Cross-sector collaborations are measured by how often each pair of sectors co-occurs within the same paper.
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Which domains are edge LMs deployed to?
In order to map the domains in which an edge LM is deployed, we perform a round of classification by tagging each paper according to their domain: Agriculture, Climate, Finance, Healthcare, Legal, Social, and Speech. Then, we extract mentions of different methods by keyword matching via KeyBERT, and visualize the domain-method connections as a network graph. Click on any outer domain node to see representative papers for that domain.
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Recommendations
- For NLP researchers and model developers: Evaluate edge models beyond memory (e.g., compute and energy), and explore underrepresented methods since current deployments cluster around a relatively narrow toolkit.
- For deployment practitioners and communities at the edge: Build cross-sector collaborations (academia, industry, research collectives, government), and involve local communities as active collaborators in development and deployment.
- For policymakers and funders: Invest not only in model development but also in infrastructure and devices that make deployment feasible in linguistically diverse, lower-resource settings; increase public-sector participation in edge LM efforts.
Citation
@misc{miranda2026multilingualityedgedevelopinglanguage,
title={{M}ultilinguality at the {E}dge: {D}eveloping {L}anguage {M}odels for the {G}lobal {S}outh},
author={Lester James Validad Miranda and Songbo Hu and Roi Reichart and Anna Korhonen},
year={2026},
eprint={2604.21637},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2604.21637},
}
Discussion
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