@inproceedings{anikina-etal-2025-rigorous,
    title = "A Rigorous Evaluation of {LLM} Data Generation Strategies for Low-Resource Languages",
    author = "Anikina, Tatiana  and
      Cegin, Jan  and
      Simko, Jakub  and
      Ostermann, Simon",
    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.418/",
    pages = "8293--8314",
    ISBN = "979-8-89176-332-6",
    abstract = "Large Language Models (LLMs) are increasingly used to generate synthetic textual data for training smaller specialized models. However, a comparison of various generation strategies for low-resource language settings is lacking. While various prompting strategies have been proposed{---}such as demonstrations, label-based summaries, and self-revision{---}their comparative effectiveness remains unclear, especially for low-resource languages. In this paper, we systematically evaluate the performance of these generation strategies and their combinations across 11 typologically diverse languages, including several extremely low-resource ones. Using three NLP tasks and four open-source LLMs, we assess downstream model performance on generated versus gold-standard data. Our results show that strategic combinations of generation methods {---} particularly target-language demonstrations with LLM-based revisions {---} yield strong performance, narrowing the gap with real data to as little as 5{\%} in some settings. We also find that smart prompting techniques can reduce the advantage of larger LLMs, highlighting efficient generation strategies for synthetic data generation in low-resource scenarios with smaller models."
}