Elamur Research⌕
☰
home / papers / 2025 / Scaling Low-Resource MT via Synthetic Data Generation with LLMs

Scaling Low-Resource MT via Synthetic Data Generation with LLMs

Paper · 2025 · Ona de Gibert, Joseph Attieh, Teemu Vahtola et al.

Summary

Most of the world's languages lack enough human-translated parallel text to train strong machine translation systems. The authors test whether LLM-generated translations can fill this gap. They used GPT-4o to translate English Europarl, paragraph by paragraph, into seven low-resource languages: Basque, Scottish Gaelic, Icelandic, Georgian, Macedonian, Somali and Ukrainian. They then split the output into sentences, filtered it with language identification and aligned it back to the English source, keeping document structure. Because Europarl is multi-way parallel, English served as a pivot to derive 147 further language pairs. Automatic metrics and human judgments rated the corpus as generally good. Compact models trained only on this data rivaled NLLB, and fine-tuning existing systems (OPUS-MT, NLLB-200-1.3B, Llama-3B) improved them consistently. The data also complemented HPLT. The corpus is released as Europarl v8syn within a new repository, SynOPUS. This shows a practical, if costly at about $5,000, route to MT resources for underrepresented languages, including Ukrainian.

Key points

  • The corpus has roughly 2–2.3 million aligned sentence pairs per language. Language-ID filtering discarded only about 0.45% of segments. Hallucinated lines about GPT-4o's training cutoff were removed: about 9,000 in Georgian and about 4,000 in Ukrainian.
  • An English→Georgian model trained from scratch on synthetic data reached 49.49 ChrF, compared with 48.31 for NLLB.
  • On average, fine-tuning raised NLLB by +2.95 ChrF and Llama-3B by +20.63 ChrF. Combining the data with HPLT added up to +2.79 ChrF for English–Icelandic.
  • Fine-tuned models 10–20 times smaller than SOTA systems matched or beat them. In a pivoted Finnish–Somali case study, gains reached +14.78 and +21.64 ChrF.
  • The authors acknowledge several limitations: a narrow parliamentary domain, possible contamination of FLORES+ or Europarl in GPT-4o's training data, and human evaluation limited to 100 samples per language pair.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of CambridgeCambridge · United Kingdom243 works · 121,396 citations🎓University of HelsinkiHelsinki · Finland50 works · 5,318 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 2Research subjects and languages the work is about.Machine translation · 1,087 worksLow-resource languages · 267 works

Cite

Cite as Ona de Gibert, Joseph Attieh, Teemu Vahtola, Mikko Aulamo, Zihao Li, Raúl Vázquez, Tiancheng Hu and Jörg Tiedemann. 2025. Scaling Low-Resource MT via Synthetic Data Generation with LLMs. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.emnlp-main.1408

@inproceedings{gibert2025scaling,
  title = {{Scaling Low-Resource MT via Synthetic Data Generation with LLMs}},
  author = {Ona {de Gibert}, Joseph Attieh, Teemu Vahtola, Mikko Aulamo, Zihao Li, Raúl Vázquez, Tiancheng Hu and Jörg Tiedemann},
  booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
  year = {2025},
  doi = {10.18653/v1/2025.emnlp-main.1408},
  url = {https://doi.org/10.18653/v1/2025.emnlp-main.1408}
}

References

61 references, 21 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Abubakar Abid, Ali Abdalla, Ali Abid, Dawood Khan, Abdulrahman Alfozan, and James Zou. 2019. Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild. arXiv preprint arXiv:1906.02569. — [arXiv:1906.02569](https://arxiv.org/abs/1906.02569) find
  2. Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Shujian Huang, Matthias Huck, Philipp Koehn, Qun Liu, Varvara Logacheva, et al. 2017. Findings of the 2017 Conference on Machine Translation (WMT17). In Proceedings of the Second Conference on Machine Translation, pag…
  3. Laurie Burchell, Ona de Gibert, Nikolay Arefyev, Mikko Aulamo, Marta Bañón, Pinzhen Chen, Mariia Fedorova, Liane Guillou, Barry Haddow, Jan Hajič, Jindřich Helcl, Erik Henriksson, Mateusz Klimaszewski, Ville Komulainen, Andrey Kutuzov, Joona Kytöniemi, Veronika Laippala, Petter Mæhlum, Bhavitvya M… — An Expanded Massive Multilingual Dataset for High-Performance Language Technologies (HPLT)
  4. Yong Cheng. 2019. Joint Training for Pivot-Based Neural Machine Translation. In Joint training for neural machine translation, pages 41–54. Springer. find
  5. Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Édouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020. Unsupervised Cross-lingual Representation Learning at Scale. In Proceedings of the 58th Annual Meeting of the Association fo… find
  6. Marta R Costa-jussá, Noé Casas, and Maite Melero. 2018. English-Catalan Neural Machine Translation in the Biomedical Domain through the Cascade Approach. arXiv preprint arXiv:1803.07139. — [arXiv:1803.07139](https://arxiv.org/abs/1803.07139) find
  7. Ona de Gibert, Graeme Nail, Nikolay Arefyev, Marta Bañón, Jelmer van der Linde, Shaoxiong Ji, Jaume Zaragoza-Bernabeu, Mikko Aulamo, Gema Ramírez-Sánchez, Andrey Kutuzov, Sampo Pyysalo, Stephan Oepen, and Jörg Tiedemann. 2024. A new massive multilingual dataset for high-performance language technolo…
  8. Ona de Gibert, Robert Pugh, Ali Marashian, Raul Vazquez, Abteen Ebrahimi, Pavel Denisov, Enora Rice, Edward Gow-Smith, Juan Prieto, Melissa Robles, Rubén Manrique, Oscar Moreno, Angel Lino, Rolando Coto-Solano, Aldo Alvarez, Marvin Agüero-Torales, John E. Ortega, Luis Chiruzzo, Arturo Oncevay, Shrut… find
  9. Alou Dembele, Nouhoum Souleymane Coulibaly, and Michael Leventhal. 2025. The Serendipity of Claude AI: Case of the 13 Low-Resource National Languages of Mali. arXiv preprint arXiv:2503.03380. — [arXiv:2503.03380](https://arxiv.org/abs/2503.03380) find
  10. Bosheng Ding, Chengwei Qin, Ruochen Zhao, Tianze Luo, Xinze Li, Guizhen Chen, Wenhan Xia, Junjie Hu, Anh Tuan Luu, and Shafiq Joty. 2024. Data Augmentation using LLMs: Data perspectives, learning paradigms and challenges. In Findings of the Association for Computational Linguistics: ACL 2024, pages… find
  11. Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024. The Llama 3 Herd of Models.
  12. Maxim Enis and Mark Hopkins. 2024. From LLM to NMT: Advancing Low-Resource Machine Translation with Claude. arXiv preprint arXiv:2404.13813.
  13. Marzieh Fadaee, Arianna Bisazza, and Christof Monz. 2017. Data Augmentation for Low-Resource Neural Machine Translation. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 567–573, Vancouver, Canada. Association for Computationa…
  14. Mitchell A Gordon and Kevin Duh. 2019. Explaining Sequence-Level Knowledge Distillation as Data-Augmentation for Neural Machine Translation. arXiv preprint arXiv:1912.03334. — [arXiv:1912.03334](https://arxiv.org/abs/1912.03334) find
  15. Naman Goyal, Cynthia Gao, Vishrav Chaudhary, Peng-Jen Chen, Guillaume Wenzek, Da Ju, Sanjana Krishnan, Marc’Aurelio Ranzato, Francisco Guzmán, and Angela Fan. 2022. The Flores-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation. Transactions of the Association for Computat… find
  16. Yvette Graham, Timothy Baldwin, Alistair Moffat, and Justin Zobel. 2013. Continuous Measurement Scales in Human Evaluation of Machine Translation. In Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse, pages 33–41. find
  17. Barry Haddow, Rachel Bawden, Antonio Valerio Miceli Barone, Jindřich Helcl, and Alexandra Birch. 2022. Survey of Low-Resource Machine Translation. Computational Linguistics, 48(3):673–732.
  18. Daniel Han, Michael Han, and Unsloth team. 2023. Unsloth. find
  19. Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. 2022. LoRA: Low-Rank Adaptation of Large Language Models. ICLR, 1(2):3.
  20. Vivek Iyer, Bhavitvya Malik, Pavel Stepachev, Pinzhen Chen, Barry Haddow, and Alexandra Birch. 2024. Quality or quantity? on data scale and diversity in adapting large language models for low-resource translation. In Proceedings of the Ninth Conference on Machine Translation, pages 1393–1409, Miami,…
  21. Tommi Jauhiainen, Heidi Jauhiainen, and Krister Lindén. 2022. HeLI-OTS, Off-the-Shelf Language Identifier for Text. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 3912–3922, Marseille, France. European Language Resources Association. find
  22. Shaoxiong Ji, Zihao Li, Indraneil Paul, Jaakko Paavola, Peiqin Lin, Pinzhen Chen, Dayyán O’Brien, Hengyu Luo, Hinrich Schütze, Jörg Tiedemann, et al. 2024. Emma-500: Enhancing Massively Multilingual Adaptation of Large Language Models. arXiv preprint arXiv:2409.17892. — [arXiv:2409.17892](https://arxiv.org/abs/2409.17892) find
  23. Wenxiang Jiao, Wenxuan Wang, Jen-tse Huang, Xing Wang, Shuming Shi, and Zhaopeng Tu. 2023. Is ChatGPT a Good Translator? Yes with GPT-4 as the Engine. arXiv preprint arXiv:2301.08745.
  24. Melvin Johnson, Mike Schuster, Quoc V Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda Viégas, Martin Wattenberg, Greg Corrado, et al. 2017. Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation. Transactions of the Association for Computational L…
  25. Marcin Junczys-Dowmunt, Kenneth Heafield, Hieu Hoang, Roman Grundkiewicz, and Anthony Aue. 2018. Marian: Cost-effective High-Quality Neural Machine Translation in C++. In Proceedings of the 2nd Workshop on Neural Machine Translation and Generation, pages 129–135. find
  26. Vanlalmuansangi Khenglawt, Sahinur Rahman Laskar, Partha Pakray, and Ajoy Kumar Khan. 2024. Addressing Data Scarcity Issue for English–Mizo Neural Machine Translation Using Data Augmentation and Language Model. Journal of Intelligent & Fuzzy Systems, 46(3):6313–6323. find
  27. Yoon Kim and Alexander M Rush. 2016. Sequence-Level Knowledge Distillation. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1317–1327.
  28. Wei-Jen Ko, Ahmed El-Kishky, Adithya Renduchintala, Vishrav Chaudhary, Naman Goyal, Francisco Guzmán, Pascale Fung, Philipp Koehn, and Mona Diab. 2021. Adapting High-Resource NMT Models to Translate Low-Resource Related Languages Without Parallel Data. In Proceedings of the 59th Annual Meeting of th… find
  29. Tom Kocmi, Vilém Zouhar, Eleftherios Avramidis, Roman Grundkiewicz, Marzena Karpinska, Maja Popović, Mrinmaya Sachan, and Mariya Shmatova. 2024. Error Span Annotation: A Balanced Approach for Human Evaluation of Machine Translation. In Proceedings of the Ninth Conference on Machine Translation, pag…
  30. Philipp Koehn. 2005. Europarl: A Parallel Corpus for Statistical Machine Translation. In Proceedings of machine translation summit x: papers, pages 79–86. find
  31. Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondřej Bojar, Alexandra Constantin, and Evan Herbst. 2007. Moses: Open Source Toolkit for Statistical Machine Translation. In Pro… find
  32. Taku Kudo and John Richardson. 2018. SentencePiece: A Simple and Language Independent Subword Tokenizer and Detokenizer for Neural Text Processing. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 66–71, Brussels, Belgium. Associ…
  33. Fethi Lamraoui and Philippe Langlais. 2013. Yet Another Fast, Robust and Open Source Sentence Aligner. Time to Reconsider Sentence Alignment? In Proceedings of Machine Translation Summit XIV: Papers, Nice, France. find
  34. Jonibek Mansurov, Akhmed Sakip, and Alham Fikri Aji. 2025. Data Laundering: Artificially Boosting Benchmark Results through Knowledge Distillation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8332–8345, Vienna, Austria. As… find
  35. Meta AI. 2022. NLLB-200-distilled-1.3B: Hugging face model card. https://huggingface.co/ facebook/nllb-200-distilled-1.3B. ↗
  36. Benjamin Minixhofer, Jonas Pfeiffer, and Ivan Vulić. 2023. Where’s the Point? Self-Supervised Multilingual Punctuation-Agnostic Sentence Segmentation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7215–7235, Toronto, Canada… find
  37. Mihai Nadas, Laura Diosan, and Andreea Tomescu. 2025. Synthetic Data Generation Using Large Language Models: Advances in Text and Code. arXiv preprint arXiv:2503.14023. — [arXiv:2503.14023](https://arxiv.org/abs/2503.14023) find
  38. Official Statistics of Finland (OSF). 2024. Number of Foreign-Language Speakers Exceeded 600,000 during 2024. https://stat.fi/en/publication/ cm1jg8tr20lco07vwvoif9s6i. Accessed: 2025-04-25. ↗
  39. Seokjin Oh, Su Ah Lee, and Woohwan Jung. 2023. Data Augmentation for Neural Machine Translation using Generative Language Model. arXiv preprint arXiv:2307.16833. — [arXiv:2307.16833](https://arxiv.org/abs/2307.16833) find
  40. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. BLEU: a Method for Automatic Evaluation of Machine Translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pages 311–318, Philadelphia, Pennsylvania, USA. Association for Computational…
  41. Maja Popović. 2015. ChrF: Character N-gram F-score for Automatic MT Evaluation. In Proceedings of the Tenth Workshop on Statistical Machine Translation, pages 392–395, Lisbon, Portugal. Association for Computational Linguistics.
  42. Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He. 2020. DeepSpeed: System Optimizations Enable Training Deep Learning Models with Over 100 Billion Parameters. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD ’20, page 3505–3506,… find
  43. Ricardo Rei, Craig Stewart, Ana C Farinha, and Alon Lavie. 2020. COMET: A Neural Framework for MT Evaluation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2685–2702, Online. Association for Computational Linguistics.
  44. Ricardo Rei, Marcos Treviso, Nuno M. Guerreiro, Chrysoula Zerva, Ana C Farinha, Christine Maroti, José G. C. de Souza, Taisiya Glushkova, Duarte Alves, Luisa Coheur, Alon Lavie, and André F. T. Martins. 2022. find
  45. CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Task. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 634–645, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
  46. Barbara Scalvini, Iben Nyholm Debess, Annika Simonsen, and Hafsteinn Einarsson. 2025. Rethinking Low-Resource MT: The Surprising Effectiveness of Fine-Tuned Multilingual Models in the LLM Age. In Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference… find
  47. Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016. Improving Neural Machine Translation Models with Monolingual Data. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 86–96. find
  48. Allahsera Auguste Tapo, Kevin Assogba, Christopher M Homan, M. Mustafa Rafique, and Marcos Zampieri. 2025. Bayelemabaga: Creating resources for Bambara NLP. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language T… find
  49. Brian Thompson, Mehak Dhaliwal, Peter Frisch, Tobias Domhan, and Marcello Federico. 2024. A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelism. In Findings of the Association for Computational Linguistics: ACL 2024, pages 1763–1775, Bangkok, Thailand. Association f… find
  50. Jörg Tiedemann, Mikko Aulamo, Daria Bakshandaeva, Michele Boggia, Stig-Arne Grönroos, Tommi Nieminen, Alessandro Raganato, Yves Scherrer, Raúl Vázquez, and Sami Virpioja. 2024. Democratizing Neural Machine Translation with OPUS-MT. Language Resources and Evaluation, 58(2):713–755. find
  51. Jörg Tiedemann and Ona De Gibert. 2023. The OPUS-MT Dashboard–A Toolkit for a Systematic Evaluation of Open Machine Translation Models. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 315–327. find
  52. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. Advances in Neural Information Processing Systems, 30. ings of the 57th Annual Meeting of the Association for Computational Linguistics, pages…
  53. Wayne Yang and Garrett Nicolai. 2023. Neural Machine Translation Data Generation and Augmentation Using Chatgpt. arXiv preprint arXiv:2307.05779. — [arXiv:2307.05779](https://arxiv.org/abs/2307.05779) find
  54. Jaume Zaragoza-Bernabeu, Gema Ramírez-Sánchez, Marta Bañón, and Sergio Ortiz Rojas. 2022. Bicleaner AI: Bicleaner goes neural. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 824–831, Marseille, France. European Language Resources Association. find
  55. Yue Zhou, Chenlu Guo, Xu Wang, Yi Chang, and Yuan Wu. 2024. A Survey on Data Augmentation in Large Model Era. arXiv preprint arXiv:2401.15422. — [arXiv:2401.15422](https://arxiv.org/abs/2401.15422) find
  56. Wenhao Zhu, Hongyi Liu, Qingxiu Dong, Jingjing Xu, Shujian Huang, Lingpeng Kong, Jiajun Chen, and Lei Li. 2024. Multilingual machine translation with large language models: Empirical results and analysis. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 2765–2781, Mexi…
  57. Barret Zoph, Deniz Yuret, Jonathan May, and Kevin Knight. 2016. Transfer Learning for Low-Resource Neural Machine Translation. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1568–1575. find
  58. Raul Vazquez, Timothee Mickus, Elaine Zosa, Teemu Vahtola, Jörg Tiedemann, Aman Sinha, Vincent Segonne, Fernando Sanchez Vega, Alessandro Raganato, Jindřich Libovický, Jussi Karlgren, Shaoxiong Ji, Jindřich Helcl, Liane Guillou, Ona De Gibert, Jaione Bengoetxea, Joseph Attieh, and Marianna Apidian… find
  59. Jiayi Wang, Yao Lu, Maurice Weber, Max Ryabinin, David Adelani, Yihong Chen, Raphael Tang, and Pontus Stenetorp. 2025. Multilingual Language Model Pretraining using Machine-translated Data. arXiv preprint arXiv:2502.13252. find
  60. Ke Wang, Jiahui Zhu, Minjie Ren, Zeming Liu, Shiwei Li, Zongye Zhang, Chenkai Zhang, Xiaoyu Wu, Qiqi Zhan, Qingjie Liu, et al. 2024. A Survey on Data Synthesis and Augmentation for Large Language Models. arXiv preprint arXiv:2410.12896. — [arXiv:2410.12896](https://arxiv.org/abs/2410.12896) find
  61. Mengzhou Xia, Xiang Kong, Antonios Anastasopoulos, and Graham Neubig. 2019. Generalized Data Augmentation for Low-Resource Translation. In Proceed-

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/gibert-2025-scaling-low-resource-mt-synthetic/