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Synthetic Instruction Generation for Low-Resource Nordic Languages: Viability and Limitations in LLM Instruction-Tuning

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.