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            <title>Proceedings of the Eleventh Conference on Machine Translation</title>
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    <abstract>We describe the TUMHN team’s systems for the WMT26 Shared Tasks on LLMs with Lim- ited Resources for Slavic Languages. We partic- ipated in the Ukrainian track, covering machine translation, question answering, spell checking, grammar checking, and math reasoning tasks. We restructured our training dataset into a chat-style format and fine-tuned the Qwen3.5-2B model for the Ukrainian track in a multitask learn- ing setting. Our model outperforms the base- line on three of the five tasks, with significant improvements on Question Answering, Spell Checking, and Grammar Checking. In Machine Translation, the model shows a slight improve- ment for the English–Ukrainian direction. For Spell Checking and Grammar Checking specif- ically, the largest gains were observed in spell or grammatical error detection as opposed to correction. In Math Reasoning, however, our model does not outperform the baseline. The fine-tuned model 1 and training dataset 2 are publicly available on Hugging Face.</abstract>
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%0 Conference Proceedings
%T TUMHN@WMT2026: LLMs with Limited Resources for Slavic Languages
%A Temesgen, Tsedeniya Kinfe
%A Kurtyigit, Sinan
%A Fraser, Alexander
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Eleventh Conference on Machine Translation
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-474-3
%F temesgen-etal-2026-tumhn
%X We describe the TUMHN team’s systems for the WMT26 Shared Tasks on LLMs with Lim- ited Resources for Slavic Languages. We partic- ipated in the Ukrainian track, covering machine translation, question answering, spell checking, grammar checking, and math reasoning tasks. We restructured our training dataset into a chat-style format and fine-tuned the Qwen3.5-2B model for the Ukrainian track in a multitask learn- ing setting. Our model outperforms the base- line on three of the five tasks, with significant improvements on Question Answering, Spell Checking, and Grammar Checking. In Machine Translation, the model shows a slight improve- ment for the English–Ukrainian direction. For Spell Checking and Grammar Checking specif- ically, the largest gains were observed in spell or grammatical error detection as opposed to correction. In Math Reasoning, however, our model does not outperform the baseline. The fine-tuned model 1 and training dataset 2 are publicly available on Hugging Face.
%U https://aclanthology.org/2026.wmt-main.212/
%P 2682-2688

Markdown (Informal)

[TUMHN@WMT2026: LLMs with Limited Resources for Slavic Languages](https://aclanthology.org/2026.wmt-main.212/) (Temesgen et al., WMT 2026)

• TUMHN@WMT2026: LLMs with Limited Resources for Slavic Languages (Temesgen et al., WMT 2026)

ACL

• Tsedeniya Kinfe Temesgen, Sinan Kurtyigit, and Alexander Fraser. 2026. TUMHN@WMT2026: LLMs with Limited Resources for Slavic Languages. In Proceedings of the Eleventh Conference on Machine Translation, pages 2682–2688, Budapest, Hungary. Association for Computational Linguistics.