@inproceedings{saadi-etal-2025-jgu,
title = "{JGU} Mainz{'}s Submission to the {WMT}25 Shared Task on {LLM}s with Limited Resources for {S}lavic Languages: {MT} and {QA}",
author = "Saadi, Hossain Shaikh and
Bui, Minh Duc and
Sanz-Guerrero, Mario and
Von Der Wense, Katharina",
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-1.89/",
pages = "1151--1157",
ISBN = "979-8-89176-341-8",
abstract = "This paper presents the JGU Mainz submission to the WMT25 Shared Task on LLMs with Limited Resources for Slavic Languages: Machine Translation and Question Answering, focusing on Ukrainian, Upper Sorbian, and Lower Sorbian. For each language, we jointly fine-tune a Qwen2.5-3B-Instruct model for both tasks with parameter-efficient finetuning. Our pipeline integrates additional translation and multiple-choice question answering (QA) data. For Ukrainian QA, we further use retrieval-augmented generation. We also apply ensembling for QA in Upper and Lower Sorbian. Experiments show that our models outperform the baseline on both tasks."
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<abstract>This paper presents the JGU Mainz submission to the WMT25 Shared Task on LLMs with Limited Resources for Slavic Languages: Machine Translation and Question Answering, focusing on Ukrainian, Upper Sorbian, and Lower Sorbian. For each language, we jointly fine-tune a Qwen2.5-3B-Instruct model for both tasks with parameter-efficient finetuning. Our pipeline integrates additional translation and multiple-choice question answering (QA) data. For Ukrainian QA, we further use retrieval-augmented generation. We also apply ensembling for QA in Upper and Lower Sorbian. Experiments show that our models outperform the baseline on both tasks.</abstract>
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%0 Conference Proceedings
%T JGU Mainz’s Submission to the WMT25 Shared Task on LLMs with Limited Resources for Slavic Languages: MT and QA
%A Saadi, Hossain Shaikh
%A Bui, Minh Duc
%A Sanz-Guerrero, Mario
%A Von Der Wense, Katharina
%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 saadi-etal-2025-jgu
%X This paper presents the JGU Mainz submission to the WMT25 Shared Task on LLMs with Limited Resources for Slavic Languages: Machine Translation and Question Answering, focusing on Ukrainian, Upper Sorbian, and Lower Sorbian. For each language, we jointly fine-tune a Qwen2.5-3B-Instruct model for both tasks with parameter-efficient finetuning. Our pipeline integrates additional translation and multiple-choice question answering (QA) data. For Ukrainian QA, we further use retrieval-augmented generation. We also apply ensembling for QA in Upper and Lower Sorbian. Experiments show that our models outperform the baseline on both tasks.
%U https://aclanthology.org/2025.wmt-1.89/
%P 1151-1157
Markdown (Informal)
[JGU Mainz’s Submission to the WMT25 Shared Task on LLMs with Limited Resources for Slavic Languages: MT and QA](https://aclanthology.org/2025.wmt-1.89/) (Saadi et al., WMT 2025)
ACL