DeMeVa at LeWiDi-2025: Modeling Perspectives with In-Context Learning
  and Label Distribution Learning
Authors: name: Daniil Ignatev; name: Nan Li; name: Hugh Mee Wong; name: Anh Dang; name: Shane Kaszefski Yaschuk
Published: 2025-09-11 15:04:42

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.