6 papers
Beyond Confidence: Rethinking Self-Assessments for Performance Prediction in LLMs
Sree Bhattacharyya, Samarth Khanna, Leona Chen +3
Large Language Models (LLMs) are increasingly used in settings where reliable self-assessment is critical. Assessing model reliability has evolved from using probabilistic correctn…
Expressing Social Emotions: Misalignment Between LLMs and Human Cultural Emotion Norms
Sree Bhattacharyya, Manas Mehta, Leona Chen +4
The expression of emotions that serve social purposes, such as asserting independence or fostering interdependence, is central to human interactions and varies systematically acros…
Large language models show fragile cognitive reasoning about human emotions
Sree Bhattacharyya, Evgenii Kuriabov, Lucas Craig +4
Affective computing seeks to support the holistic development of artificial intelligence by enabling machines to engage with human emotion. Recent foundation models, particularly l…
Unsupervised Memorability Modeling from Tip-of-the-Tongue Retrieval Queries
Sree Bhattacharyya, Yaman Kumar Singla, Sudhir Yarram +3
Visual content memorability has intrigued the scientific community for decades, with applications ranging widely, from understanding nuanced aspects of human memory to enhancing co…
Evaluating Vision-Language Models for Emotion Recognition
Sree Bhattacharyya, James Z. Wang
Large Vision-Language Models (VLMs) have achieved unprecedented success in several objective multimodal reasoning tasks. However, to further enhance their capabilities of empatheti…
A Heterogeneous Multimodal Graph Learning Framework for Recognizing User Emotions in Social Networks
Sree Bhattacharyya, Shuhua Yang, James Z. Wang
The rapid expansion of social media platforms has provided unprecedented access to massive amounts of multimodal user-generated content. Comprehending user emotions can provide val…