@inproceedings{ignatev-etal-2025-demeva,
    title = "{D}e{M}e{V}a at {L}e{W}i{D}i-2025: Modeling Perspectives with In-Context Learning and Label Distribution Learning",
    author = "Ignatev, Daniil  and
      Li, Nan  and
      Wong, Hugh Mee  and
      Dang, Anh  and
      Yaschuk, Shane Kaszefski",
    editor = "Abercrombie, Gavin  and
      Basile, Valerio  and
      Frenda, Simona  and
      Tonelli, Sara  and
      Dudy, Shiran",
    booktitle = "Proceedings of the The 4th Workshop on Perspectivist Approaches to NLP",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.nlperspectives-1.15/",
    pages = "171--181",
    ISBN = "979-8-89176-350-0",
    abstract = "This system paper presents the DeMeVa team{'}s approaches to the third edition of the Learning with Disagreements shared task (LeWiDi 2025; Leonardelli et al., 2025). We explore two directions: in-context learning (ICL) with large language models, where we compare example sampling strategies; and label distribution learning (LDL) methods with RoBERTa (Liu et al., 2019b), where we evaluate several fine-tuning methods. Our contributions are twofold: (1) we show that ICL can effectively predict annotator-specific annotations (perspectivist annotations), and that aggregating these predictions into soft labels yields competitive performance; and (2) we argue that LDL methods are promising for soft label predictions and merit further exploration by the perspectivist community."
}