@inproceedings{sainz-etal-2025-instructing,
    title = "Instructing Large Language Models for Low-Resource Languages: A Systematic Study for {B}asque",
    author = "Sainz, Oscar  and
      Perez, Naiara  and
      Etxaniz, Julen  and
      Fernandez de Landa, Joseba  and
      Aldabe, Itziar  and
      Garc{\'i}a-Ferrero, Iker  and
      Zabala, Aimar  and
      Azurmendi, Ekhi  and
      Rigau, German  and
      Agirre, Eneko  and
      Artetxe, Mikel  and
      Soroa, Aitor",
    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.1484/",
    pages = "29124--29148",
    ISBN = "979-8-89176-332-6",
    abstract = "Instructing language models with user intent requires large instruction datasets, which are only available for a limited set of languages. In this paper, we explore alternatives to conventional instruction adaptation pipelines in low-resource scenarios. We assume a realistic scenario for low-resource languages, where only the following are available: corpora in the target language, existing open-weight multilingual base and instructed backbone LLMs, and synthetically generated instructions sampled from the instructed backbone. We present a comprehensive set of experiments for Basque that systematically study different combinations of these components evaluated on benchmarks and human preferences from 1,680 participants. Our conclusions show that target language corpora are essential, with synthetic instructions yielding robust models, and, most importantly, that using as backbone an instruction-tuned model outperforms using a base non-instructed model. Scaling up to Llama 3.1 Instruct 70B as backbone, our model comes near frontier models of much larger sizes for Basque, without using any Basque instructions. We release code, models, instruction datasets, and human preferences to support full reproducibility in future research on low-resource language adaptation."
}