mlsanityData to 7 Oct 2026

Scaling Low-Resource MT via Synthetic Data Generation with LLMs

arXiv 2505.14423 ↗First posted 20 May 2025cs.CLcited by 14

On the map: Synthetic data and self-improvement for LLMs → LLM synthetic data generation

Abstract (arXiv)

We investigate the potential of LLM-generated synthetic data for improving low-resource Machine Translation (MT). Focusing on seven diverse target languages, we construct a document-level synthetic corpus from English Europarl, and extend it via pivoting to 147 additional language pairs. Automatic and human evaluation confirm its overall high quality. We study its practical application by (i) identifying effective training regimes, (ii) comparing our data with the HPLT dataset, (iii) studying the effect of varying training data size, and (iiii) testing its utility beyond English-centric MT. Finally, we introduce SynOPUS, a public repository for synthetic parallel datasets. Our findings show that LLM-generated synthetic data, even when noisy, can substantially improve MT performance for low-resource languages.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Placed in LLM synthetic data generation, one of 13 directions in Synthetic data and self-improvement for LLMs. Builds on 37 of its 62 references that are in the map. First posted on arXiv 20 May 2025.

Builds on

37 of 62 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Synthetic Data Generation Using Large Language Models: Advances in Text and Code18 Mar

Other references · 36

  1. 2017Attention Is All You Needcited by 59,932
  2. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  3. 2018SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processingcited by 1,973
  4. 2020COMET: A Neural Framework for MT Evaluationcited by 1,024
  5. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 784
  6. 2016Sequence-Level Knowledge Distillationcited by 761
  7. 2021The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translationcited by 556
  8. 2016Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translationcited by 467
  9. 2022CometKiwi: IST-Unbabel 2022 Submission for the Quality Estimation Shared Taskcited by 205
  10. 2016Transfer Learning for Low-Resource Neural Machine Translationcited by 188
  11. 2019Gradio: Hassle-Free Sharing and Testing of ML Models in the Wildcited by 176
  12. 2023Is ChatGPT A Good Translator? Yes With GPT-4 As The Enginecited by 172
24 more
  1. 2017Data Augmentation for Low-Resource Neural Machine Translationcited by 139
  2. 2021Survey of Low-Resource Machine Translationcited by 92
  3. 2024Error Span Annotation: A Balanced Approach for Human Evaluation of Machine Translationcited by 62
  4. 2022Democratizing Neural Machine Translation with OPUS-MTcited by 57
  5. 2024From LLM to NMT: Advancing Low-Resource Machine Translation with Claudecited by 57
  6. 2024A New Massive Multilingual Dataset for High-Performance Language Technologiescited by 56
  7. 2019Generalized Data Augmentation for Low-Resource Translationcited by 53
  8. 2024A Shocking Amount of the Web is Machine Translated: Insights from Multi-Way Parallelismcited by 33
  9. 2024A Survey on Data Augmentation in Large Model Eracited by 31
  10. 2024Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challengescited by 30
  11. 2016Neural Machine Translation with Pivot Languagescited by 25
  12. 2018Marian: Cost-effective High-Quality Neural Machine Translation in C++cited by 22
  13. 2021Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Datacited by 17
  14. 2023Where's the Point? Self-Supervised Multilingual Punctuation-Agnostic Sentence Segmentationcited by 17
  15. 2025Multilingual Language Model Pretraining using Machine-translated Datacited by 17
  16. 2019Explaining Sequence-Level Knowledge Distillation as Data-Augmentation for Neural Machine Translationcited by 16
  17. 2024EMMA-500: Enhancing Massively Multilingual Adaptation of Large Language Modelscited by 16
  18. 2025An Expanded Massive Multilingual Dataset for High-Performance Language Technologies (HPLT)cited by 15
  19. 2024Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for Low-Resource Translationcited by 13
  20. 2023Data Augmentation for Neural Machine Translation using Generative Language Modelcited by 12
  21. 2024Data Laundering: Artificially Boosting Benchmark Results through Knowledge Distillationcited by 6
  22. 2023Neural Machine Translation Data Generation and Augmentation using ChatGPTcited by 5
  23. 2018English-Catalan Neural Machine Translation in the Biomedical Domain through the cascade approachcited by 1
  24. 2025The Serendipity of Claude AI: Case of the 13 Low-Resource National Languages of Malicited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 11.

14 papers.

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  1. 2025HPLT 3.0: Very Large-Scale Multilingual Resources for LLMs and MT. Mono- and Bi-lingual Data, Multilingual Evaluation, and Pre-Trained Modelscited by 6
  2. 2026KD4MT: A Survey of Knowledge Distillation for Machine Translationcited by 2
  3. 2025OpenWHO: A Document-Level Parallel Corpus for Health Translation in Low-Resource Languagescited by 1
  4. 2026Translation Asymmetry in LLMs as a Data Augmentation Factor: A Case Study for 6 Romansh Language Varietiescited by 1
  5. 2026AFRILANGTUTOR: Advancing Language Tutoring and Culture Education in Low-Resource Languages with Large Language Modelscited by 1
  6. 2025AI-Driven Generation of Old English: A Framework for Low-Resource Languagescited by 0
  7. 2026An Empirical Study of On-Device Translation for Real-Time Live-Stream Chat on Mobile Devicescited by 0
  8. 2026Open Machine Translation for Esperantocited by 0
  9. 2026Bridging the Stability-Expressivity Gap: Synthetic Data Scaling and Preference Alignment for Low-Resource Spoken Language Modelscited by 0
  10. 2026Data Synthesis and Parameter-Efficient Fine-Tuning for Low-Resource NMT: A Case Study on Q'eqchi' Mayancited by 0
  11. 2026CoPiT: Cognitive Pivot Translation for Digraphic Low-Resource Mongolian in the Traditional Scriptcited by 0
  12. 2026A Factorial Study of Synthetic Data Generation for Low-Resource Machine Translation using Grammar Bookscited by 0
2 more
  1. 2026Cross-Lingual Transfer for Machine Translation in Turkic Languagescited by 0
  2. 2026TransClean: A Benchmark for Detecting and Extracting Clean Translations from Large Language Model Outputscited by 0

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  1. 2026Better as Generators Than Classifiers: Leveraging LLMs and Synthetic Data for Low-Resource Multilingual Classification
  2. 2025Demystifying Synthetic Data in LLM Pre-training: A Systematic Study of Scaling Laws, Benefits, and Pitfalls
  3. 2026Enhancing Document-Level Machine Translation via Filtered Synthetic Corpora and Two-Stage LLM Adaptation
  4. 2025Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation
  5. 2024On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey
  6. 2025Scaling Laws of Synthetic Data for Language Models
  7. 2025BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation
  8. 2026Less is More: Adapting Text Embeddings for Low-Resource Languages with Small Scale Noisy Synthetic Data