mlsanityData to 14 Oct 2026

JGU Mainz's Submission to the WMT25 Shared Task on LLMs with Limited Resources for Slavic Languages: MT and QA

arXiv 2509.22490 ↗First posted 26 Sep 2025cs.CLcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 16 of its 29 references that are in the map. First posted on arXiv 26 Sep 2025.

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16 of 29 references.

References · 16

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,886
  3. 2019HellaSwag: Can a Machine Really Finish Your Sentence?cited by 4,100
  4. 2019PIQA: Reasoning about Physical Commonsense in Natural Languagecited by 2,883
  5. 2019BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questionscited by 2,183
  6. 2018Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answeringcited by 2,164
  7. 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,822
  8. 2018A Call for Clarity in Reporting BLEU Scorescited by 1,799
  9. 2017RACE: Large-scale ReAding Comprehension Dataset From Examinationscited by 861
  10. 2017Crowdsourcing Multiple Choice Science Questionscited by 765
  11. 2023Large Language Models Are Not Robust Multiple Choice Selectorscited by 486
  12. 2019Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoningcited by 285
4 more
  1. 2019QASC: A Dataset for Question Answering via Sentence Compositioncited by 252
  2. 2019DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehensioncited by 156
  3. 2018When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?cited by 126
  4. 2024In-Context Example Selection via Similarity Search Improves Low-Resource Machine Translationcited by 22

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Citing papers per year: 2025: 1.

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