@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://aclanthology.org/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."
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%0 Conference Proceedings
%T DeMeVa at LeWiDi-2025: Modeling Perspectives with In-Context Learning and Label Distribution Learning
%A Ignatev, Daniil
%A Li, Nan
%A Wong, Hugh Mee
%A Dang, Anh
%A Yaschuk, Shane Kaszefski
%Y Abercrombie, Gavin
%Y Basile, Valerio
%Y Frenda, Simona
%Y Tonelli, Sara
%Y Dudy, Shiran
%S Proceedings of the The 4th Workshop on Perspectivist Approaches to NLP
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-350-0
%F ignatev-etal-2025-demeva
%X 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.
%U https://aclanthology.org/2025.nlperspectives-1.15/
%P 171-181
Markdown (Informal)
[DeMeVa at LeWiDi-2025: Modeling Perspectives with In-Context Learning and Label Distribution Learning](https://aclanthology.org/2025.nlperspectives-1.15/) (Ignatev et al., NLPerspectives 2025)
ACL