@inproceedings{de-gibert-etal-2025-scaling,
    title = "Scaling Low-Resource {MT} via Synthetic Data Generation with {LLM}s",
    author = {de Gibert, Ona  and
      Attieh, Joseph  and
      Vahtola, Teemu  and
      Aulamo, Mikko  and
      Li, Zihao  and
      V{\'a}zquez, Ra{\'u}l  and
      Hu, Tiancheng  and
      Tiedemann, J{\"o}rg},
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1408/",
    pages = "27662--27680",
    ISBN = "979-8-89176-332-6",
    abstract = "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."
}