11 citations · 29 across the 32 of their papers we have counts for
37 papers
GENDIS: Gender-Related Discourse and Stance in Multilingual YouTube Comments from India, Kenya, and Nigeria
Luise Koch, Manuel Tonneau, Niyati Malhotra +5
Research on gender-related online content is often operationalized through harm-centered tasks that collapse non-harmful gender-related and unrelated content, while existing datase…
AI-Assisted Submissions in Online Research Are Rare and Highly Concentrated but Routinely Approved
Neil K. R. Sehgal, Manuel Tonneau, Dunigan Folk +2
Online research platforms underpin much of what science claims about people, on the assumption that a human produced each response. Generative AI threatens that assumption by letti…
AgentWorld: Benchmarking Long-Horizon Collaboration of Multi-agent LLMs
Raphael Shu, Yusen Zhang, Young Min Cho +7
Existing multi-agent benchmarks primarily test in competitive settings, short-horizon interactions under 20 steps, or simply aggregate individual performance, failing to isolate an…
"We Are Tired of Explaining": Communication Practice and AI Roleplay Training for Community Health Workers in Rural India
Neil K. R. Sehgal, Sunny Rai, Sai Preethi Matam +4
Community health workers (CHWs) in the Global South increasingly encounter AI-powered tools, yet the counseling work central to their role remains largely unsupported. We study com…
LLMs Mirror Country-Specific Gender Patterns If Asked, but Skew Male When Generating Media in Local Languages
Sharif Kazemi, Tanya Popli, Neil K. R. Sehgal +7
Large language models (LLMs) are increasingly used to generate media, but whether their content perpetuates gender stereotypes is unknown: standard benchmarks rely on selection-bas…
Predictors of Loneliness in Older Adults Using Multimodal Analysis of Speech and Language
Vinmay Khandode, Sai Karthik Kosuri, Neil K. R. Sehgal +6
Loneliness is a critical public health issue among older adults, linked to higher risks of depression, cognitive decline, and mortality. Scalable, objective methods for its detecti…
Beyond Right and Wrong: Evaluating Second-order Social Reasoning in Large Language Models
Sunny Rai, Jinyi Kuang, Reyhan Jamalova +7
Previous AI alignment efforts have focused primarily on first-order social norms -- teaching models what is socially acceptable or unacceptable (e.g., `do not steal'). However, soc…
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…
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…
What Users Cannot See: Evaluating LLM Emotional Support Beyond User Preference
Vivienne Bihe Chi, Adithya V Ganesan, Ryan L Boyd +2
People increasingly turn to LLMs for emotional support, yet common evaluations reward responses that feel helpful and may miss consequential response behaviors. We introduce a theo…
Optimized but Unowned: How AI-Authored Goals Undermine the Motivation They Are Meant to Drive
Vivienne Bihe Chi, Roman Rietsche, Andreas Göldi +2
As AI tools become embedded in productivity and self-improvement contexts, a pressing question emerges: what happens when AI does the goal-setting for us? In a preregistered experi…
Conceptors for Semantic Steering
Ilias Triantafyllopoulos, Young-Min Cho, Ren Tao +6
Activation-based steering provides control of LLM behavior at inference time, but the dominant paradigm reduces each concept to a single direction whose geometry is left largely un…
Self-Reported Side Effects of Semaglutide and Tirzepatide in Online Communities
Neil K. R. Sehgal, Jena Shaw Tronieri, Lyle Ungar +1
Social media can reveal patient experiences with glucagon-like peptide-1 receptor agonists (GLP-1 RAs) that extend beyond clinical trial data. We analyzed 410,198 Reddit posts (May…
Voice-Based Chatbots for English Speaking Practice in Multilingual Low-Resource Indian Schools: A Multi-Stakeholder Study
Sneha Shashidhara, Vivienne Bihe Chi, Abhay P Singh +2
Spoken English proficiency is a powerful driver of economic mobility for low-income Indian youth, yet opportunities for spoken practice remain scarce in schools. We investigate the…
A Concise Agent is Less Expert: Revealing Side Effects of Using Style Features on Conversational Agents
Young-Min Cho, Yuan Yuan, Sharath Chandra Guntuku +1
Style features such as friendly, helpful, or concise are widely used in prompts to steer the behavior of Large Language Model (LLM) conversational agents, yet their unintended side…
Different Demographic Cues Yield Inconsistent Conclusions About LLM Personalization and Bias
Manuel Tonneau, Neil K. R. Sehgal, Niyati Malhotra +7
Demographic cue-based evaluation is widely used to study how large language models (LLMs) adapt their responses to signaled demographic attributes within and across groups. This ap…
Real-Time Deadlines Reveal Fragile Temporal Adaptation in LLM Strategic Dialogues
Neil K. R. Sehgal, Sharath Chandra Guntuku, Lyle Ungar
Large Language Models (LLMs) generate text token-by-token in discrete time, yet real-world communication, from therapy sessions to business negotiations, critically depends on cont…
Designing Mental-Health Chatbots for Indian Adolescents: Mixed-Methods Evidence, a Boundary-Object Lens, and a Design-Tensions Framework
Neil K. R. Sehgal, Hita Kambhamettu, Sai Preethi Matam +2
Mental health challenges among Indian adolescents are shaped by unique cultural and systemic barriers, including high social stigma and limited professional support. We report a mi…
Beyond the Strongest LLM: Multi-Turn Multi-Agent Orchestration vs. Single LLMs on Benchmarks
Aaron Xuxiang Tian, Ruofan Zhang, Jiayao Tang +12
We study multi-turn multi-agent orchestration, where multiple large language model (LLM) agents interact over multiple turns by iteratively proposing answers or casting votes until…
Effects of Static and Conversational AI-Generated Messages on Colorectal Cancer Screening Intent: a Randomized Clinical Trial
Neil K. R. Sehgal, Manuel Tonneau, Andy SL Tan +6
Large language model chatbots show increasing promise in persuasive communication, but their clinical utility remains uncertain, particularly in settings where sustained conversati…
PAL: Designing Conversational Agents as Scalable, Cooperative Patient Simulators for Palliative-Care Training
Neil K. R. Sehgal, Hita Kambhamettu, Allen Chang +3
Effective communication in serious illness and palliative care is essential but often under-taught due to limited access to training resources like standardized patients. We presen…
Herd Behavior: Investigating Peer Influence in LLM-based Multi-Agent Systems
Young-Min Cho, Sharath Chandra Guntuku, Lyle Ungar
Recent advancements in Large Language Models (LLMs) have enabled the emergence of multi-agent systems where LLMs interact, collaborate, and make decisions in shared environments. W…
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…
Exploring Socio-Cultural Challenges and Opportunities in Designing Mental Health Chatbots for Adolescents in India
Neil K. R. Sehgal, Hita Kambhamettu, Sai Preethi Matam +2
Mental health challenges among Indian adolescents are shaped by unique cultural and systemic barriers, including high social stigma and limited professional support. Through a mixe…