LNLP – 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗜𝘀𝘀𝘂𝗲 𝗼𝗻 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 𝗳𝗼𝗿 𝗟𝗼𝘄-𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗟𝗮𝗻𝗴

𝗝𝗼𝘂𝗿𝗻𝗮𝗹 𝗡𝗮𝘁𝘂𝗿𝗮𝗹 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 – 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗜𝘀𝘀𝘂𝗲 𝗼𝗻 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 𝗳𝗼𝗿 𝗟𝗼𝘄-𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀

URL – https://loreslm.github.io/specialissue

Neural language models have revolutionised natural language processing (NLP) and have provided state-of-the-art results for many tasks. However, their effectiveness is largely dependent on the pre-training resources. Therefore, language models (LMs) often struggle with low-resource languages in both training and evaluation. Recently, there has been a growing trend in developing and adopting LMs for low-resource languages. This special issue aims to provide a forum for researchers to share and discuss their ongoing work on LMs for low-resource languages.

𝗧𝗼𝗽𝗶𝗰𝘀

We invite submissions on a broad range of topics related to the development and evaluation of neural language models for low-resource languages, including but not limited to the following.

– Building language models for low-resource languages.

– Adapting/extending existing language models/large language models for low-resource languages.

– Corpora creation and curation technologies for training language models/large language models for low-resource languages.

– Benchmarks to evaluate language models/large language models in low-resource languages.

– Prompting/in-context learning strategies for low-resource languages with large language models.

– Review of available corpora to train/fine-tune language models/large language models for low-resource languages.

– Multilingual/cross-lingual language models/large language models for low-resource languages.

– Applications of language models/large language models for low-resource languages (i.e. machine translation, chatbots, content moderation, etc.)

𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗗𝗮𝘁𝗲𝘀

Paper submission: December 31, 2025

First decision: March 31, 2026- April 30, 2026

Revised version submission: May 1, 2026- June 1, 2026

Final decision: August 30, 2026

𝗦𝘂𝗯𝗺𝗶𝘀𝘀𝗶𝗼𝗻

Submissions should be formatted according to the journal guidelines available – https://www.cambridge.org/core/journals/natural-language-processing/information/author-instructions/preparing-your-materials and submitted through the manuscript submission system – https://mc.manuscriptcentral.com/nlp. To ensure your manuscript is considered for this special issue, please select “Language Models for Low-Resource Languages” under Special Issue Designation when uploading your manuscript.

Guest Editors

Hansi Hettiarachchi, Lancaster University, UK

Tharindu Ranasinghe, Lancaster University, UK

Paul Rayson, Lancaster University, UK

Ruslan Mitkov, Lancaster University, UK

Mohamed Gaber, Queensland University of Technology, Australia

Guest Editorial Board

Gábor Bella – IMT Atlantique, France

Ana-Maria Bucur – University of Bucharest, Romania

Çağrı Çöltekin – University of Tübingen, Germany

Vera Danilova – Uppsala University, Sweden

Ona de Gibert – University of Helsinki, Finland

Ignatius Ezeani – Lancaster University, UK

Amal Htait – Aston University, UK

Ali Hürriyetoğlu – Wageningen University & Research, Netherlands

Danka Jokic – University of Belgrade, Serbia

Diptesh Kanojia – University of Surrey, UK

Taro Watanabe – Nara Institute of Science and Technology, Japan

Muhidin Mohamed – Aston University, UK

Alistair Plum – University of Luxembourg, Luxembourg

Damith Premasiri – Lancaster University, UK

Guokan Shang – Mohamed bin Zayed University of Artificial Intelligence, France

Ravi Shekhar – University of Essex, UK