Low-Resource Languages in the Age of Large Language Models

Dear Colleagues,

The rapid progress of large language models (LLMs) has transformed natural language processing, but this progress has been strikingly uneven across the world’s languages. While high-resource languages benefit from increasingly capable models, many low-resource and endangered languages remain severely under-served, risking further digital marginalisation and loss of linguistic diversity. This Special Issue focuses on methods, resources, and evaluations that place low-resource languages at the centre of the LLM agenda rather than at its periphery.

We seek submissions that advance the theory and practice of NLP for low-resource languages in an era dominated by large-scale neural models. Relevant work includes novel techniques for learning under extreme data scarcity, effective use of multilingual and foundation models, creation and curation of corpora and benchmarks, and studies that critically assess how well current models represent, preserve, or distort low-resource languages and their linguistic structures. We particularly welcome contributions that combine solid empirical methodology with an explicit concern for linguistic diversity, equity, and long-term sustainability.

Topics of interest include, but are not limited to, the following:

• Architectures, training regimes, and adaptation strategies for LLMs targeting low-resource languages;
• Multilingual and cross-lingual transfer, prompting, and fine-tuning for data-scarce settings;
• Creation, documentation, and evaluation of corpora, lexicons, treebanks, and benchmarks for low-resource languages;
• Methods for modelling morphology, syntax, semantics, and discourse in typologically diverse languages;
• Human–in–the–loop, active learning, and participatory approaches with speaker communities;
• Evaluation of LLMs on low-resource languages, including robustness, bias, and fairness analyses;
• Applications in education, translation, information access, and language preservation or revitalisation.

This Special Issue aims to provide a forum where computational methods and linguistic insights jointly contribute to a more inclusive future for language technology in the age of large language models.

We look forward to receiving your valuable contributions.

Dr. Colin Layfield
Dr. John Abela
Guest Editors

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• natural language processing
• LLMs
• NLP