@inproceedings{ilinykh-szawerna-2025-need,
    title = "``{I} Need More Context and an {E}nglish Translation'': Analysing How {LLM}s Identify Personal Information in {K}omi, {P}olish, and {E}nglish",
    author = "Ilinykh, Nikolai  and
      Szawerna, Maria Irena",
    editor = "Holdt, {\v{S}}pela Arhar  and
      Ilinykh, Nikolai  and
      Scalvini, Barbara  and
      Bruton, Micaella  and
      Debess, Iben Nyholm  and
      Tudor, Crina Madalina",
    booktitle = "Proceedings of the Third Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL-2025)",
    month = mar,
    year = "2025",
    address = "Tallinn, Estonia",
    publisher = "University of Tartu Library, Estonia",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.resourceful-1.32/",
    pages = "165--178",
    ISBN = "978-9908-53-121-2",
    abstract = "Automatic identification of personal information (PI) is particularly difficult for languages with limited linguistic resources. Recently, large language models (LLMs) have been applied to various tasks involving low-resourced languages, but their capability to process PI in such contexts remains under-explored. In this paper we provide a qualitative analysis of the outputs from three LLMs prompted to identify PI in texts written in Komi (Permyak and Zyrian), Polish, and English. Our analysis highlights challenges in using pre-trained LLMs for PI identification in both low- and medium-resourced languages. It also motivates the need to develop LLMs that understand the differences in how PI is expressed across languages with varying levels of availability of linguistic resources."
}