@article{stenlund-etal-2026-synthetic,
    title = "Synthetic Instruction Generation for Low-Resource {N}ordic Languages: Viability and Limitations in {LLM} Instruction-Tuning",
    author = "Stenlund, Mathias  and
      Simonsen, Annika  and
      Bungum, Lars  and
      Ebert, Jan  and
      Wang, Jiangtao  and
      Filatov, Oleg  and
      Myneni, Hemanadhan  and
      Riedel, Morris  and
      Einarsson, Hafsteinn",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.838/",
    pages = "10688--10698",
    abstract = "Pretrained large language models (LLMs) gain instruction-following abilities through instruction-tuning, a method which relies on datasets of instruction{--}response pairs. However, for low-resource languages, collecting human-authored instructions is costly, raising the question of whether synthetic instructions can substitute human-authored instructions for non-English languages. We compare instruction-tuning of a smaller pretrained LLM in four Nordic languages using (a) human-authored instructions paired with synthetic responses and (b) fully synthetic instruction{--}response pairs generated with a minimal-effort pipeline. Native-speaker evaluations show that models instruction-tuned on synthetic instructions perform on par with those trained on human-authored instructions for the largest Nordic languages, suggesting that minimal-effort synthetic instructions can serve as a practical alternative. In contrast, response quality deteriorates sharply for Icelandic, underscoring the limitations of current synthetic data generation pipelines when the LLM competence in the target language is weak. Overall, our results highlight that while synthetic instructions can enable cost-efficient instruction-tuning for the largest Nordic languages, they remain insufficient for Icelandic, clarifying when minimal-effort synthetic approaches suffice and when they fall short."
}