@inproceedings{okabe-etal-2025-findings,
title = "Findings of the {WMT} 2025 Shared Task {LLM}s with Limited Resources for {S}lavic Languages: {MT} and {QA}",
author = "Okabe, Shu and
Dementieva, Daryna and
Di Marco, Marion and
Edman, Lukas and
Haemmerl, Katharina and
M{\v{e}}{\v{s}}kank, Marko and
Hendrichowa, Anita and
Fraser, Alexander",
editor = "Haddow, Barry and
Kocmi, Tom and
Koehn, Philipp and
Monz, Christof",
booktitle = "Proceedings of the Tenth Conference on Machine Translation",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.wmt-1.27/",
pages = "503--519",
ISBN = "979-8-89176-341-8",
abstract = "We present the findings of the WMT 2025 Shared Task LLMs with Limited Resources for Slavic Languages. This shared task focuses on training LLMs using limited data and compute resources for three Slavic languages: Upper Sorbian (hsb), Lower Sorbian (dsb), and Ukrainian (uk), with the objective to develop and improve LLMs for these languages. We consider two tasks which are to be evaluated jointly: Machine Translation (MT) and Multiple-Choice Question Answering (QA). In total, three teams participated in this shared task, with submissions from all three teams for the Sorbian languages and one submission for Ukrainian. All submissions led to an improvement compared to the baseline Qwen2.5-3B model through varying fine-tuning strategies. We note, however, that training purely on MT degrades original QA capabilities. We also report further analyses on the submissions, including MT evaluation using advanced neural metrics for Ukrainian, as well as manual annotation and comparison to the current Sorbian machine translator."
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<abstract>We present the findings of the WMT 2025 Shared Task LLMs with Limited Resources for Slavic Languages. This shared task focuses on training LLMs using limited data and compute resources for three Slavic languages: Upper Sorbian (hsb), Lower Sorbian (dsb), and Ukrainian (uk), with the objective to develop and improve LLMs for these languages. We consider two tasks which are to be evaluated jointly: Machine Translation (MT) and Multiple-Choice Question Answering (QA). In total, three teams participated in this shared task, with submissions from all three teams for the Sorbian languages and one submission for Ukrainian. All submissions led to an improvement compared to the baseline Qwen2.5-3B model through varying fine-tuning strategies. We note, however, that training purely on MT degrades original QA capabilities. We also report further analyses on the submissions, including MT evaluation using advanced neural metrics for Ukrainian, as well as manual annotation and comparison to the current Sorbian machine translator.</abstract>
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%0 Conference Proceedings
%T Findings of the WMT 2025 Shared Task LLMs with Limited Resources for Slavic Languages: MT and QA
%A Okabe, Shu
%A Dementieva, Daryna
%A Di Marco, Marion
%A Edman, Lukas
%A Haemmerl, Katharina
%A Měškank, Marko
%A Hendrichowa, Anita
%A Fraser, Alexander
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Tenth Conference on Machine Translation
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-341-8
%F okabe-etal-2025-findings
%X We present the findings of the WMT 2025 Shared Task LLMs with Limited Resources for Slavic Languages. This shared task focuses on training LLMs using limited data and compute resources for three Slavic languages: Upper Sorbian (hsb), Lower Sorbian (dsb), and Ukrainian (uk), with the objective to develop and improve LLMs for these languages. We consider two tasks which are to be evaluated jointly: Machine Translation (MT) and Multiple-Choice Question Answering (QA). In total, three teams participated in this shared task, with submissions from all three teams for the Sorbian languages and one submission for Ukrainian. All submissions led to an improvement compared to the baseline Qwen2.5-3B model through varying fine-tuning strategies. We note, however, that training purely on MT degrades original QA capabilities. We also report further analyses on the submissions, including MT evaluation using advanced neural metrics for Ukrainian, as well as manual annotation and comparison to the current Sorbian machine translator.
%U https://aclanthology.org/2025.wmt-1.27/
%P 503-519
Markdown (Informal)
[Findings of the WMT 2025 Shared Task LLMs with Limited Resources for Slavic Languages: MT and QA](https://aclanthology.org/2025.wmt-1.27/) (Okabe et al., WMT 2025)
ACL
- Shu Okabe, Daryna Dementieva, Marion Di Marco, Lukas Edman, Katharina Haemmerl, Marko Měškank, Anita Hendrichowa, and Alexander Fraser. 2025. Findings of the WMT 2025 Shared Task LLMs with Limited Resources for Slavic Languages: MT and QA. In Proceedings of the Tenth Conference on Machine Translation, pages 503–519, Suzhou, China. Association for Computational Linguistics.