@inproceedings{seweryn-etal-2025-pllum,
    title = "{PLL}u{M}-Align: {P}olish Preference Dataset for Large Language Model Alignment",
    author = "Seweryn, Karolina  and
      Ko{\l}os, Anna  and
      Karli{\'n}ska, Agnieszka  and
      Lorenc, Katarzyna  and
      Dziewulska, Katarzyna  and
      Chrabaszcz, Maciej  and
      Krasnodebska, Aleksandra  and
      Betscher, Paula  and
      Cie{\'s}li{\'n}ska, Zofia  and
      Kowol, Katarzyna  and
      Moska, Julia  and
      Motyka, Dawid  and
      Walkowiak, Pawe{\l}  and
      {\.Z}uk, Bartosz  and
      Janz, Arkadiusz",
    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.1219/",
    pages = "23890--23919",
    ISBN = "979-8-89176-332-6",
    abstract = "Alignment is the critical process of minimizing harmful outputs by teaching large language models (LLMs) to prefer safe, helpful and appropriate responses. While the majority of alignment research and datasets remain overwhelmingly English-centric, ensuring safety across diverse linguistic and cultural contexts requires localized resources. In this paper, we introduce the first Polish preference dataset PLLuM-Align, created entirely through human annotation to reflect Polish language and cultural nuances. The dataset includes response rating, ranking, and multi-turn dialog data. Designed to reflect the linguistic subtleties and cultural norms of Polish, this resource lays the groundwork for more aligned Polish LLMs and contributes to the broader goal of multilingual alignment in underrepresented languages."
}