[Workshop website - https://loreslm.github.io/home]
[CFP - https://loreslm.github.io/cfp]
[Direct submission - https://openreview.net/group?id=eacl.org/EACL/2027/Workshop/LoResLM]
[ARR commitment - https://openreview.net/group?id=eacl.org/EACL/2027/Workshop/LoResLM_ARR_Commitment]

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 2027 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 2027 standards for submission format and guidelines. LoResLM 2027 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 2027 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

We accept both direct submissions and commitments of papers already reviewed through ACL Rolling Review (ARR). Papers should be submitted through OpenReview using the following links:
• Direct submissions - https://openreview.net/group?id=eacl.org/EACL/2027/Workshop/LoResLM
• ARR commitments - https://openreview.net/group?id=eacl.org/EACL/2027/Workshop/LoResLM_ARR_Commitment

𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗗𝗮𝘁𝗲𝘀
• Paper submission (direct and ARR commitment): 7th January 2027
• Notification of acceptance: 28th January 2027
• Camera-ready submission: 4th February 2027
• Workshop: March 2027 (TBD) @ EACL

𝗩𝗲𝗻𝘂𝗲
LoResLM 2027 will be held in conjunction with EACL 2027 in Athens, Greece.

𝗣𝗿𝗼𝗰𝗲𝗲𝗱𝗶𝗻𝗴𝘀
Proceedings of the workshop will appear in ACL Anthology. For 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
Lasitha Uyangodage – University of Münster, Germany

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

𝗖𝗼𝗻𝘁𝗮𝗰𝘁 𝘂𝘀
Contact us through loreslm.contact@gmail.com.
Follow us on social media
• LinkedIn - https://www.linkedin.com/company/loreslm/
• X - https://x.com/LoResLM2027
• BlueSky - https://bsky.app/profile/loreslm.bsky.social