@inproceedings{wroblewska-2025-poleval,
    title = "{P}ol{E}val 2025 Task 2: Gender-inclusive {LLM}s for {P}olish",
    author = "Wr{\'o}blewska, Alina",
    editor = "Kobyli{\'n}ski, {\L}ukasz  and
      Wr{\'o}blewska, Alina  and
      Ogrodniczuk, Maciej",
    booktitle = "Proceedings of the {P}ol{E}val 2025 Workshop",
    month = nov,
    year = "2025",
    address = "Warsaw",
    publisher = "Institute of Computer Science PAS and Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-paclic/2025.poleval-main.6/",
    pages = "39--47",
    abstract = "This paper presents the results of the PolEval 2025 shared task on gender-inclusive large language models for Polish. The primary goal of this task is to encourage the development of models capable of generating grammatically well-formed, contextually appropriate, and gender-inclusive output {---} a property of increasing importance in both human-centred NLP and NLG applications. To support this objective, we employed the newly developed Inclusive Polish Instruction Set (IPIS), a high-quality, human-annotated resource designed to guide models toward gender-inclusive behaviour. The shared task comprised two subtasks: gender-inclusive proofreading, which evaluates the ability of a model to transform masculine-generic Polish text into an inclusive equivalent, and gender-sensitive Polish-English translation, which investigates gender marking across languages. A total of six system submissions were received {---} three for each subtask. The evaluation demonstrates that the top-performing gender-inclusive systems outperform both the baseline and state-of-the-art models. These findings highlight the effectiveness of IPIS-tuned approaches and establish strong benchmarks for future research on gender inclusivity in Polish NLP."
}