9 citations · 9 across the 4 of their papers we have counts for
5 papers
Discourse Diversity in Multi-Turn Empathic Dialogue
Hongli Zhan, Emma S. Gueorguieva, Javier Hernandez +3
Large language models (LLMs) produce responses rated as highly empathic in single-turn settings (Ayers et al., 2023; Lee et al., 2024), yet they are also known to be formulaic gene…
AI generates well-liked but templatic empathic responses
Emma S. Gueorguieva, Hongli Zhan, Jina Suh +4
Recent research shows that greater numbers of people are turning to Large Language Models (LLMs) for emotional support, and that people rate LLM responses as more empathic than hum…
When Testing AI Tests Us: Safeguarding Mental Health on the Digital Frontlines
Sachin R. Pendse, Darren Gergle, Rachel Kornfield +6
Red-teaming is a core part of the infrastructure that ensures that AI models do not produce harmful content. Unlike past technologies, the black box nature of generative AI systems…
Large Language Models are Capable of Offering Cognitive Reappraisal, if Guided
Hongli Zhan, Allen Zheng, Yoon Kyung Lee +3
Large language models (LLMs) have offered new opportunities for emotional support, and recent work has shown that they can produce empathic responses to people in distress. However…
Large Language Models Produce Responses Perceived to be Empathic
Yoon Kyung Lee, Jina Suh, Hongli Zhan +2
Large Language Models (LLMs) have demonstrated surprising performance on many tasks, including writing supportive messages that display empathy. Here, we had these models generate…