565 citations · 565 across the 6 of their papers we have counts for
8 papers · 1 filter
From Vision to Language: Investigating Causal Information Flow in Multimodal Decision-Making
Davide Testa, Hugh Mee Wong, Alessandro Lenci +2
Vision-Language Models are commonly evaluated through their final predictions, but understanding whether these decisions are grounded in visual evidence requires tracing how visual…
Investigating Multimodal Informativity under Different Partner Visibility Conditions in Video-Mediated Dialogue
Esam Ghaleb, Hugh Mee Wong, Kristina Kobrock
Situated language use is multimodal and embodied. For example, gestures can carry information that is absent or underspecified in the speech signal, yet dialogue models typically r…
When Models Decide and When They Bind: A Two-Stage Computation for Multiple-Choice Question-Answering
Hugh Mee Wong, Rick Nouwen, Albert Gatt
Multiple-choice question answering (MCQA) is easy to evaluate but adds a meta-task: models must both solve the problem and output the symbol that *represents* the answer, conflatin…
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…
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…
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…