6 papers
Choose Your Agent: Tradeoffs in Adopting AI Advisors, Coaches, and Delegates in Multi-Party Negotiation
Kehang Zhu, Nithum Thain, Vivian Tsai +2
As AI usage becomes more prevalent in social contexts, understanding agent-user interaction is critical to designing systems that imp rove both individual and group outcomes. We pr…
Real-Time Group Dynamics with LLM Facilitation: Evidence from a Charity Allocation Task
Aaron Parisi, Nithum Thain, Alden Hallak +2
As large language models (LLMs) evolve from single-user assistants to active participants in civic and workplace deliberation, evaluating their effects on collective decision makin…
Strategic Tradeoffs Between Humans and AI in Multi-Agent Bargaining
Crystal Qian, Kehang Zhu, John Horton +4
Markets increasingly accommodate large language models (LLMs) as autonomous decision-making agents. As this transition occurs, it becomes critical to evaluate how these agents beha…
Deliberate Lab: A Platform for Real-Time Human-AI Social Experiments
Crystal Qian, Vivian Tsai, Michael Behr +4
Social and behavioral scientists increasingly aim to study how humans interact, collaborate, and make decisions alongside artificial intelligence. However, the experimental infrast…
Improving Neutral Point-of-View Generation with Data- and Parameter-Efficient RL
Jessica Hoffmann, Christiane Ahlheim, Zac Yu +8
The paper shows that parameter-efficient reinforcement learning (PE-RL) is a highly effective training regime to improve large language models' (LLMs) ability to answer queries on…
Thinking Like a Scientist: Can Interactive Simulations Foster Critical AI Literacy?
Yiling Zhao, Audrey Michal, Nithum Thain +1
As AI systems shape individual and societal decisions, fostering critical AI literacy is essential. Traditional approaches, such as blog articles, static lessons, and social media…