44 citations · 49 across the 11 of their papers we have counts for
9 papers · 1 filter
Social Priors: Descriptions of AI Users Shape How Language Models Trust and Cooperate
Harsh Kumar, George Eilender, Ashton Anderson
Descriptions of how people use AI are becoming training data for the next generation of models. We ask whether what a model reads about its users changes how it treats people. Usin…
Emulate or Estimate? The Divergent Strengths of Base and Post-Trained Language Models for Opinion Simulation
Seth Grief-Albert, Jessica Bo, Difan Jiao +1
Large language models are increasingly used to simulate human opinions, but prior work reports conflicting results: some studies find promising alignment with human survey data, wh…
Position: We Need Large Language Models Optimized For Our Well-Being
Ashton Anderson, Harsh Kumar, Louis Tay +1
Large language models are useful because we taught them to give us what we want. This works when success can be judged immediately, but people increasingly bring these systems thei…
Studying People to Study AI: Expert Perspectives on the Epistemic Fit and Barriers of Human Research in AI Safety & Ethics
Jessica Y. Bo, Paula Akemi Aoyagui, Shalaleh Rismani +3
Safety risks of AI are becoming increasingly evident in human interactions with AI technologies. The prominent approaches to evaluating these risks favor technical methods, such as…
Diagnosing and Repairing Persona Collapse in LLM Advice
Harsh Kumar, Karina Vold, Louis Tay +1
A good advisor comforts someone in crisis, challenges someone in denial, and gives practical guidance on a logistical question. We ask whether large language models (LLMs) vary the…
Human Thinking under Plural LLM Assistance: Mathematical Problem Solving and Open-Ended Writing
Harsh Kumar, Jace Mu, Jonathan Vincentius +1
Large language models are changing not only the kind of assistance people receive, but also how that assistance is organized. Instead of working with a single general-purpose chatb…
The Social Sycophancy Scale: A psychometrically validated measure of sycophancy
Jean Rehani, Victoria Oldemburgo de Mello, Dariya Ovsyannikova +2
Large Language Model (LLM) sycophancy is a growing concern. The current literature has largely examined sycophancy in contexts with clear right and wrong answers, like coding. Howe…
Language Models Exhibit Inconsistent Biases Towards Algorithmic Agents and Human Experts
Jessica Y. Bo, Lillio Mok, Ashton Anderson
Large language models are increasingly used in decision-making tasks that require them to process information from a variety of sources, including both human experts and other algo…
Large Language Lovers: Lived Experiences of Negotiating Agency and Platform Control in AI Companionship
Patrick Yung Kang Lee, Jessica Y. Bo, Zixin Zhao +6
Individuals are turning to increasingly anthropomorphic, general-purpose chatbots for AI companionship, rather than roleplay-specific platforms. However, not much is known about ho…