2.3k citations · 2.7k across the 7 of their papers we have counts for
7 papers
Shaping the Emerging Norms of Using Large Language Models in Social Computing Research
Hong Shen, Tianshi Li, Toby Jia-Jun Li +2
The emergence of Large Language Models (LLMs) has brought both excitement and concerns to social computing research. On the one hand, LLMs offer unprecedented capabilities in analy…
Generative Agents: Interactive Simulacra of Human Behavior
Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai +3
Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping too…
Measuring the Prevalence of Anti-Social Behavior in Online Communities
Joon Sung Park, Joseph Seering, Michael S. Bernstein
With increasing attention to online anti-social behaviors such as personal attacks and bigotry, it is critical to have an accurate accounting of how widespread anti-social behavior…
Social Simulacra: Creating Populated Prototypes for Social Computing Systems
Joon Sung Park, Lindsay Popowski, Carrie J. Cai +3
Social computing prototypes probe the social behaviors that may arise in an envisioned system design. This prototyping practice is currently limited to recruiting small groups of p…
Jury Learning: Integrating Dissenting Voices into Machine Learning Models
Mitchell L. Gordon, Michelle S. Lam, Joon Sung Park +4
Whose labels should a machine learning (ML) algorithm learn to emulate? For ML tasks ranging from online comment toxicity to misinformation detection to medical diagnosis, differen…
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…
Understanding the Representation and Representativeness of Age in AI Data Sets
Joon Sung Park, Michael S. Bernstein, Robin N. Brewer +2
A diverse representation of different demographic groups in AI training data sets is important in ensuring that the models will work for a large range of users. To this end, recent…