4 citations · 12 across the 16 of their papers we have counts for
18 papers
Public Perceptions and Acceptance of Socially Intelligent AI Agents
Leena Mathur, Jenny T. Liang, Vasudha Varadarajan +6
AI researchers have been advancing socially intelligent AI agents (Social-AI) across embodiments, from chatbots to physical robots. As Social-AI agents move from labs into everyday…
SOTOPIA-TOM: Evaluating Privacy and Information Management in Multi-Agent Interaction with Theory of Mind
Yashwanth YS, Ruichen Wang, Shihua Zeng +4
As LLM-based agents are increasingly interacting in multi-party settings, they need to properly handle information asymmetry, i.e., knowing when and to whom to disclose information…
On Safety Risks in Experience-Driven Self-Evolving Agents
Weixiang Zhao, Yichen Zhang, Yingshuo Wang +8
Experience-driven self-evolution has emerged as a promising paradigm for improving the autonomy of large language model agents, yet its reliance on self-curated experience introduc…
Imperfectly Cooperative Human-AI Interactions: Comparing the Impacts of Human and AI Attributes in Simulated and User Studies
Myke C. Cohen, Mingqian Zheng, Neel Bhandari +6
AI design characteristics and human personality traits each impact the quality and outcomes of human-AI interactions. However, their relative and joint impacts are underexplored in…
GoodPoint: Learning Constructive Scientific Paper Feedback from Author Responses
Jimin Mun, Chani Jung, Xuhui Zhou +2
While LLMs hold significant potential to transform scientific research, we advocate for their use to augment and empower researchers rather than to automate research without human…
The PIMMUR Principles: Ensuring Validity in Collective Behavior of LLM Societies
Jiaxu Zhou, Jen-tse Huang, Xuhui Zhou +5
Large language models (LLMs) are increasingly used to simulate human collective behavior, yet claims that such simulations are human-like remain largely untested. We conducted a sy…
Social World Models
Xuhui Zhou, Jiarui Liu, Akhila Yerukola +2
Humans intuitively navigate social interactions by simulating unspoken dynamics and reasoning about others' perspectives, even with limited information. In contrast, AI systems str…
1-2-3 Check: Enhancing Contextual Privacy in LLM via Multi-Agent Reasoning
Wenkai Li, Liwen Sun, Zhenxiang Guan +2
Addressing contextual privacy concerns remains challenging in interactive settings where large language models (LLMs) process information from multiple sources (e.g., summarizing m…
SoMi-ToM: Evaluating Multi-Perspective Theory of Mind in Embodied Social Interactions
Xianzhe Fan, Xuhui Zhou, Chuanyang Jin +3
Humans continuously infer the states, goals, and behaviors of others by perceiving their surroundings in dynamic, real-world social interactions. However, most Theory of Mind (ToM)…
Words Like Knives: Backstory-Personalized Modeling and Detection of Violent Communication
Jocelyn Shen, Akhila Yerukola, Xuhui Zhou +3
Conversational breakdowns in close relationships are deeply shaped by personal histories and emotional context, yet most NLP research treats conflict detection as a general task, o…
SOTOPIA-S4: a user-friendly system for flexible, customizable, and large-scale social simulation
Xuhui Zhou, Zhe Su, Sophie Feng +9
Social simulation through large language model (LLM) agents is a promising approach to explore and validate hypotheses related to social science questions and LLM agents behavior.…
Rethinking Theory of Mind Benchmarks for LLMs: Towards A User-Centered Perspective
Qiaosi Wang, Xuhui Zhou, Maarten Sap +2
The last couple of years have witnessed emerging research that appropriates Theory-of-Mind (ToM) tasks designed for humans to benchmark LLM's ToM capabilities as an indication of L…
How Do Users Negotiate Harmful Value Conflicts with AI Companions? A Study with Minion, a Technology Probe for In-Situ Human-AI Conflict Response
Xianzhe Fan, Qing Xiao, Xuhui Zhou +4
AI companions increasingly sustain long-term, emotionally engaging relationships but can also make discriminatory remarks or exert control, leaving users to manage harmful conflict…
BIG5-CHAT: Shaping LLM Personalities Through Training on Human-Grounded Data
Wenkai Li, Jiarui Liu, Andy Liu +3
In this work, we tackle the challenge of embedding realistic human personality traits into LLMs. Previous approaches have primarily focused on prompt-based methods that describe th…
HAICOSYSTEM: An Ecosystem for Sandboxing Safety Risks in Human-AI Interactions
Xuhui Zhou, Hyunwoo Kim, Faeze Brahman +9
AI agents are increasingly autonomous in their interactions with human users and tools, leading to increased interactional safety risks. We present HAICOSYSTEM, a framework examini…
AI-LieDar: Examine the Trade-off Between Utility and Truthfulness in LLM Agents
Zhe Su, Xuhui Zhou, Sanketh Rangreji +4
Truthfulness (adherence to factual accuracy) and utility (satisfying human needs and instructions) are both fundamental aspects of Large Language Models, yet these goals often conf…
User-Driven Value Alignment: Understanding Users' Perceptions and Strategies for Addressing Biased and Discriminatory Statements in AI Companions
Xianzhe Fan, Qing Xiao, Xuhui Zhou +4
Large language model-based AI companions are increasingly viewed by users as friends or romantic partners, leading to deep emotional bonds. However, they can generate biased, discr…
On the Resilience of LLM-Based Multi-Agent Collaboration with Faulty Agents
Jen-tse Huang, Jiaxu Zhou, Tailin Jin +6
Large language model-based multi-agent systems have shown great abilities across various tasks due to the collaboration of expert agents, each focusing on a specific domain. Howeve…