Despite impressive progress in natural language processing (NLP), most current Large Language Models (LLMs) remain heavily skewed toward high-resource languages such as English, French, or Spanish. Low-resource languages—spoken by millions across Asia, Africa, Europe, and the Americas—continue to suffer from insufficient datasets, limited computational resources, weak representation in technology, and inadequate linguistic tools. This digital division restricts access to modern AI technologies and hinders inclusive digital transformation across underserved communities.

The proposed Thematic Secction aims to bring together seminal contributions that advance the development, evaluation, and deployment of LLMs for low-resource languages. By fostering research collaborations, highlighting innovations, and sharing curated datasets, this Thematic Section will support global efforts to democratize language technologies.

Objectives of the Thematic Section:

●     Promote research on LLMs tailored for low-resource, endangered, indigenous, and marginalized languages.

●     Encourage creation and sharing of datasets, linguistic resources, and benchmarks.

●     Showcase novel architectures improving LLM performance with minimal training data.

●     Support inclusive AI by ensuring multilingual equity in NLP research.

●     Explore real-world use cases where LLMs empower communities, education, governance, healthcare, and accessibility.

●     Promote LLM research for Indian low-resource languages

●     Evaluate LLM performance on low-resource languages using standardized benchmarks.

●     LLLMs any other domains

Submission Guidelines and Deadlines:

●     Proposal Submission Deadline: Jan 30, 2026

●     First Review/Desk Rejection: March 01, 2026

●     Second Review: April 30, 2026

●     Final Acceptance: May 15, 2026

●     Final Online Publication: June, 2026

Guest Editors:

●     Partha Pakray, National Institute of Technology Silchar, Assam, India

●     Santanu Pal, Wipro Lab 45, India

●     Rabiah Abdul Kadir, UKM Malaysia

●     Riyanka Manna, Amrita Vishwa Vidyapeetham, India

Computación y Sistemas journal is indexed in Scopus, Web of Science (Emerging Sources), and Journal Citation Reports (JCR, IF=0.6).

We invite you to contribute to this thematic issue and help advance the field of NLP for low-resource languages. For inquiries, please do not hesitate to contact us.

We look forward to your valuable submissions.