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Findings of the WMT 2025 Shared Task LLMs With Limited Resources for Slavic Languages: MT and QA

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Alexander Fraser

Prof. Dr.

Principal Investigator

Data Analytics & Statistics

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.

inproceedings

WMT @EMNLP 2025

10th Conference on Machine Translation at the Conference on Empirical Methods in Natural Language Processing. Suzhou, China, Nov 04-09, 2025.

Authors

S. Okabe • D. Dementieva • M. Di Marco • L. Edman • K. Hämmerl • M. Měškank • A. Hendrichowa • A. Fraser

Links

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Research Area

 B2 | Natural Language Processing

BibTeXKey: ODD+25

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