activity
20242026
most citedExpresso-AI: Explainable Video-Based Deep Learning Models for Depression Diagnosis

3 citations · 7 across the 7 of their papers we have counts for

collaborators
Showing cs.CLShow all

8 papers · 1 filter

cs.CL20251 cited

Ensembling Large Language Models to Characterize Affective Dynamics in Student-AI Tutor Dialogues

Chenyu Zhang, Sharifa Alghowinem, Cynthia Breazeal

While recent studies have examined the leaning impact of large language model (LLM) in educational contexts, the affective dynamics of LLM-mediated tutoring remain insufficiently u…

cs.CL20241 cited

A Demonstration of Adaptive Collaboration of Large Language Models for Medical Decision-Making

Yubin Kim, Chanwoo Park, Hyewon Jeong +7

Medical Decision-Making (MDM) is a multi-faceted process that requires clinicians to assess complex multi-modal patient data patient, often collaboratively. Large Language Models (…

cs.CL2024

Can Language Models Take A Hint? Prompting for Controllable Contextualized Commonsense Inference

Pedro Colon-Hernandez, Nanxi Liu, Chelsea Joe +5

Generating commonsense assertions within a given story context remains a difficult task for modern language models. Previous research has addressed this problem by aligning commons…

cs.CL2024

EmpathicStories++: A Multimodal Dataset for Empathy towards Personal Experiences

Jocelyn Shen, Yubin Kim, Mohit Hulse +4

Modeling empathy is a complex endeavor that is rooted in interpersonal and experiential dimensions of human interaction, and remains an open problem within AI. Existing empathy dat…

cs.CL2024

HEART-felt Narratives: Tracing Empathy and Narrative Style in Personal Stories with LLMs

Jocelyn Shen, Joel Mire, Hae Won Park +2

Empathy serves as a cornerstone in enabling prosocial behaviors, and can be evoked through sharing of personal experiences in stories. While empathy is influenced by narrative cont…

cs.CL2024

Improving Dialogue Agents by Decomposing One Global Explicit Annotation with Local Implicit Multimodal Feedback

Dong Won Lee, Hae Won Park, Yoon Kim +2

We describe an approach for aligning an LLM-based dialogue agent based on global (i.e., dialogue-level) rewards, while also taking into account naturally-occurring multimodal signa…