𝗙𝗶𝗿𝘀𝘁 𝗖𝗮𝗹𝗹 𝗳𝗼𝗿 𝗣𝗮𝗽𝗲𝗿𝘀 - 𝗧𝗵𝗲 𝗦𝗲𝗰𝗼𝗻𝗱 𝗪𝗼𝗿𝗸𝘀𝗵𝗼𝗽 𝗼𝗻 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 𝗳𝗼𝗿 𝗟𝗼𝘄-𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀

[Workshop website - https://loreslm.github.io/home]
[CFP - https://loreslm.github.io/cfp]

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. Supporting this important shift, LoResLM aims to provide a forum for researchers to share and discuss their ongoing work on LMs for low-resource languages.

𝗧𝗼𝗽𝗶𝗰𝘀
LoResLM 2026 invites submissions on a broad range of topics related to the development and evaluation of neural language models for low-resource languages. We welcome research that explores modalities beyond text and encourage work on low-resource dialects in addition to major language varieties. Topics of interest include, but are not limited to:
  • 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.
  • Multimodal 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.)

𝗦𝘂𝗯𝗺𝗶𝘀𝘀𝗶𝗼𝗻 𝗚𝘂𝗶𝗱𝗲𝗹𝗶𝗻𝗲𝘀
We follow the EACL 2026 standards for submission format and guidelines. LoResLM 2026 invites submissions of long papers up to 8 pages and short papers up to 4 pages. These page limits only apply to the main body of the paper. At the end of the paper (after the conclusions but before the references), papers need to include a mandatory section discussing the limitations of the work and, optionally, a section discussing ethical considerations. Papers can include unlimited pages of references and an appendix.
To prepare your submission, please make sure to use the EACL 2026 style files available here:
  • Latex - https://github.com/acl-org/acl-style-files
  • Overleaf - https://www.overleaf.com/latex/templates/association-for-computational-linguistics-acl-conference/jvxskxpnznfj
Papers should be submitted through OpenReview using the following link: https://openreview.net/group?id=eacl.org/EACL/2026/Workshop/LoResLM

𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗗𝗮𝘁𝗲𝘀
  • Paper submission: 6th January 2026
  • Notification of acceptance: 28th January 2026
  • Camera-ready submission: 3rd February 2026
  • Workshop: March 28, 2026- March 29, 2026 (TBD) @ EACL

𝗩𝗲𝗻𝘂𝗲
LoResLM 2026 will be held in conjunction with EACL 2026 in Rabat, Morocco.

𝗣𝗿𝗼𝗰𝗲𝗲𝗱𝗶𝗻𝗴𝘀
Proceedings of the workshop will appear in ACL Anthology. For the past proceedings, please refer https://scholar.google.co.uk/citations?user=rvm3HOgAAAAJ&hl=en

𝗢𝗿𝗴𝗮𝗻𝗶𝘀𝗶𝗻𝗴 𝗖𝗼𝗺𝗺𝗶𝘁𝘁𝗲𝗲
Hansi Hettiarachchi – Lancaster University, UK
Tharindu Ranasinghe – Lancaster University, UK
Alistair Plum – University of Luxembourg, Luxembourg
Damith Premasiri – Lancaster University, UK
Fiona Anting Tan – National University of Singapore, Singapore
Lasitha Uyangodage – University of Münster, Germany

𝗔𝗱𝘃𝗶𝘀𝗼𝗿𝘀
Paul Rayson – Lancaster University, UK
Ruslan Mitkov – Lancaster University, UK
Mohamed Gaber – Queensland University of Technology, Australia

𝗦𝘂𝗽𝗽𝗼𝗿𝘁𝗲𝗱 𝗯𝘆
The workshop is supported in part by the Artificial Intelligence Journal, which promotes and disseminates AI research.

𝗖𝗼𝗻𝘁𝗮𝗰𝘁 𝘂𝘀
Contact us through loreslm.contact@gmail.com.
Follow us in social media
  • LinkedIn - https://www.linkedin.com/company/loreslm/
  • X - https://x.com/LoResLM2026
  • BlueSky - https://bsky.app/profile/loreslm.bsky.social