{"ID":2872246,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.09524","arxiv_id":"2509.09524","title":"DeMeVa at LeWiDi-2025: Modeling Perspectives with In-Context Learning and Label Distribution Learning","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.","short_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 learn...","url_abs":"https://arxiv.org/abs/2509.09524","url_pdf":"https://arxiv.org/pdf/2509.09524v1","authors":"[\"Daniil Ignatev\",\"Nan Li\",\"Hugh Mee Wong\",\"Anh Dang\",\"Shane Kaszefski Yaschuk\"]","published":"2025-09-11T15:04:42Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.LG\"]","methods":"[\"Language Model\",\"LoRA\"]","has_code":false}