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cs.CL2025
DeMeVa at LeWiDi-2025: Modeling Perspectives with In-Context Learning and Label Distribution Learning
Daniil Ignatev, Nan Li, Hugh Mee Wong +2
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…
cs.CL2025
Disentangling the Roles of Representation and Selection in Data Pruning
Yupei Du, Yingjin Song, Hugh Mee Wong +3
Data pruning, selecting small but impactful subsets, offers a promising way to efficiently scale NLP model training. However, existing methods often involve many different design c…
cs.CL2025
VAQUUM: Are Vague Quantifiers Grounded in Visual Data?
Hugh Mee Wong, Rick Nouwen, Albert Gatt
Vague quantifiers such as "a few" and "many" are influenced by various contextual factors, including the number of objects present in a given context. In this work, we evaluate the…