DeMeVa at LeWiDi-2025: Modeling Perspectives with In-Context Learning
  and Label Distribution Learning
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.
68c514daee0eed1697d6b395
Daniil Ignatev
admin_assigned
2025-09-15T15:22:01.696Z
6305d542435ec751b72434b8
/avatars/cc48bf4102cd9c959b4f2a39c7c422f4.svg
Daniil Ignatev
user
ruthenian8
68c514daee0eed1697d6b396
Nan Li
claimed_verified
2025-09-13T14:50:49.829Z
61bf8017c88f3fd22f654086
/avatars/ea9e762b3db755fe051751577353fbca.svg
Nan Li
user
chnln
68c514daee0eed1697d6b397
Hugh Mee Wong
68c514daee0eed1697d6b398
Anh Dang
68c514daee0eed1697d6b399
Shane Kaszefski Yaschuk
DeMeVa at LeWiDi-2025: Modeling Perspectives with In-Context Learning
  and Label Distribution Learning
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.
DeMeVa explores in-context learning and label distribution learning for predicting annotator-specific annotations and generating soft labels, demonstrating competitive performance and potential for further research.
in-context learning ICL label distribution learning LDL large language models example sampling strategies fine-tuning methods RoBERTa perspectivist annotations soft labels