3 papers
cs.CV2025
MagiC: Evaluating Multimodal Cognition Toward Grounded Visual Reasoning
Chengfei Wu, Ronald Seoh, Bingxuan Li +3
Recent advances in large vision-language models have led to impressive performance in visual question answering and multimodal reasoning. However, it remains unclear whether these…
cs.CL2025
EmoGist: Efficient In-Context Learning for Visual Emotion Understanding
Ronald Seoh, Dan Goldwasser
In this paper, we introduce EmoGist, a training-free, in-context learning method for performing visual emotion classification with LVLMs. The key intuition of our approach is that…
cs.CL2025
SOLAR: Towards Characterizing Subjectivity of Individuals through Modeling Value Conflicts and Trade-offs
Younghun Lee, Dan Goldwasser
Large Language Models (LLMs) not only have solved complex reasoning problems but also exhibit remarkable performance in tasks that require subjective decision making. Existing stud…