activity
20242026
most citedLarge Language Model Chatbot Conversations vs Public Health Materials and Parental HPV Vaccination Intentions: A Randomized Clinical Trial

1 citations · 1 across the 8 of their papers we have counts for

collaborators

31 papers

cs.CY2026

PersonaMem-v3: Toward Omni-Platform Personal Intelligence for Holistic User Understanding, Recommendation, and Agentic Tasks

Bowen Jiang, Yuan Yuan, Zhuoqun Hao +11

Personal intelligence is becoming a central frontier for user-facing AI agents. To be helpful in everyday life, agents must understand users across the digital contexts where their…

cs.HC2026

Depression Symptoms and Relational Patterns in 187k ChatGPT Histories

Neil K. R. Sehgal, Dunigan Folk, Lyle Ungar +1

Large language models are increasingly used as private, always-available conversational systems, but little is known about how people with depressive symptoms use them. Building on…

cs.CY20261 cited

Large Language Model Chatbot Conversations vs Public Health Materials and Parental HPV Vaccination Intentions: A Randomized Clinical Trial

Neil K. R. Sehgal, Sunny Rai, Manuel Tonneau +6

Health care systems are increasingly considering large language model (LLM)-based chatbots for vaccine communication, but evidence that they improve durable, behaviorally relevant…

cs.HC2026

Narrative Sharpens Gender Gaps: Surveying Film Characters with LLM Agents

Vivienne Bihe Chi, Reyhan Jamalova, Lyle Ungar +1

Mainstream film is one of the richest sources of cultural content that AI systems learn from. Yet we have few tools for measuring the gender values it encodes. We present a proof-o…

cs.HC2026

When Support Escalates Distress: Regulation and Escalation in LLM Responses to Venting and Advice-Seeking

Vivienne Bihe Chi, Adithya V Ganesan, Ryan L Boyd +2

Large language models are increasingly used for mental health support, yet little is known about whether their responses are psychologically safe across different help-seeking styl…

cs.CL2026

T-FIX: Text-Based Explanations with Features Interpretable to eXperts

Shreya Havaldar, Weiqiu You, Chaehyeon Kim +12

As LLMs are deployed in knowledge-intensive settings (e.g., surgery, astronomy, therapy), users are often domain experts who expect not just answers, but explanations that mirror p…