ljvmiranda921/PolyglotTeachers-SFT-Synth-Data

Tasks

• text-generation

Citation text from the card

@inproceedings{miranda-etal-2026-polyglot,
    title = "Polyglot Teachers: Evaluating Language Models for Multilingual Synthetic Data Generation",
    author = "Miranda, Lester James Validad  and
      Vuli{\'c}, Ivan  and
      Korhonen, Anna",
    editor = "Lee, Hung-Yi  and
      Dev, Sunipa  and
      Li, Jessy  and
      Elliott, Desmond",
    booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.emnlp-main.440/",
    pages = "10116--10144",
    ISBN = "979-8-89176-459-0",
    abstract = "Synthesizing supervised finetuning (SFT) data from language models (LMs) to teach smaller models multilingual tasks has become increasingly common. However, teacher model selection is often ad hoc, typically defaulting to the largest available option, even though such models may have significant capability gaps in non-English languages. This practice can result in poor-quality synthetic data and suboptimal student downstream performance. In this work, we systematically characterize what makes an effective multilingual teacher. We combine intrinsic measures of data quality with extrinsic student model performance in a metric we call Polyglot Score. We evaluate 10 LMs across 6 typologically diverse languages, generating over 1.4M SFT examples and training 240 student mode

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