๐๐ถ๐ฟ๐๐ ๐๐ฎ๐น๐น ๐ณ๐ผ๐ฟ ๐ฃ๐ฎ๐ฝ๐ฒ๐ฟ๐ – ๐ง๐ต๐ฒ ๐ฆ๐ฒ๐ฐ๐ผ๐ป๐ฑ ๐ช๐ผ๐ฟ๐ธ๐๐ต๐ผ๐ฝ ๐ผ๐ป ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ ๐ ๐ผ๐ฑ๐ฒ๐น๐ ๐ณ๐ผ๐ฟ ๐๐ผ๐-๐ฅ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ ๐๐ฎ๐ป๐ด๐๐ฎ๐ด๐ฒ๐
[Workshop website – https://loreslm.github.io/home]
[CFP – https://loreslm.github.io/cfp]
[Submission – https://openreview.net/group?id=eacl.org/EACL/2026/Workshop/LoResLM]
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.
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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:
ย ย โข Building language models for low-resource languages.
ย ย โข Adapting/extending existing language models/large language models for low-resource languages.
ย ย โข Corpora creation and curation technologies for training language models/large language models for low-resource languages.
ย ย โข Benchmarks to evaluate language models/large language models in low-resource languages.
ย ย โข Prompting/in-context learning strategies for low-resource languages with large language models.
ย ย โข Review of available corpora to train/fine-tune language models/large language models for low-resource languages.
ย ย โข Multilingual/cross-lingual language models/large language models for low-resource languages.
ย ย โข Multimodal language models/large language models for low-resource languages
ย ย โข Applications of language models/large language models for low-resource languages (i.e. machine translation, chatbots, content moderation, etc.)
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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:
ย ย โข Latex – https://github.com/acl-org/acl-style-files
ย ย โข 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
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ย ย โข Paper submission: 6th January 2026
ย ย โข Notification of acceptance: 28th January 2026
ย ย โข Camera-ready submission: 3rd February 2026
ย ย โข Workshop: 29th March, 2026 @ EACL
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LoResLM 2026 will be held in conjunction with EACL 2026 in Rabat, Morocco.
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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
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Hansi Hettiarachchi โ Lancaster University, UK
Tharindu Ranasinghe โ Lancaster University, UK
Alistair Plum โ University of Luxembourg, Luxembourg
Damith Premasiri โ Lancaster University, UK
Fiona Anting Tan โ National University of Singapore, Singapore
Lasitha Uyangodage โ University of Mรผnster, Germany
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Paul Rayson โ Lancaster University, UK
Ruslan Mitkov โ Lancaster University, UK
Mohamed Gaber โ Queensland University of Technology, Australia
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The workshop is supported in part by the Artificial Intelligence Journal, which promotes and disseminates AI research.
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Contact us through loreslm.contact@gmail.com.
Follow us in social media
ย ย โข LinkedIn – https://www.linkedin.com/company/loreslm/
ย ย โข X – https://x.com/LoResLM2026
ย ย โข BlueSky – https://bsky.app/profile/loreslm.bsky.social