10 citations · 10 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Towards Understanding Counseling Conversations: Domain Knowledge and Large Language Models
Younghun Lee, Dan Goldwasser, Laura Schwab Reese
Understanding the dynamics of counseling conversations is an important task, yet it is a challenging NLP problem regardless of the recent advance of Transformer-based pre-trained l…
Ask, and shall you receive?: Understanding Desire Fulfillment in Natural Language Text
Snigdha Chaturvedi, Dan Goldwasser, Hal Daume
The ability to comprehend wishes or desires and their fulfillment is important to Natural Language Understanding. This paper introduces the task of identifying if a desire expresse…