8 papers
Muse Spark Safety & Preparedness Report
Cristina Menghini, Peter Ney, Hamza Kwisaba +117
Muse Spark is the latest large language model developed by Meta. In this report, we first present evaluations for catastrophic risk domains under Meta's Advanced AI Scaling Framewo…
What's In My Human Feedback? Learning Interpretable Descriptions of Preference Data
Rajiv Movva, Smitha Milli, Sewon Min +1
Human feedback can alter language models in unpredictable and undesirable ways, as practitioners lack a clear understanding of what feedback data encodes. While prior work studies…
The Prosocial Ranking Challenge: Reducing Polarization on Social Media without Sacrificing Engagement
Jonathan Stray, Ian Baker, George Beknazar-Yuzbashev +42
We report the first direct comparisons of multiple alternative social media algorithms on multiple platforms on outcomes of societal interest. We used a browser extension to modify…
Cultivating Pluralism In Algorithmic Monoculture: The Community Alignment Dataset
Lily Hong Zhang, Smitha Milli, Karen Jusko +12
How can large language models (LLMs) serve users with varying preferences that may conflict across cultural, political, or other dimensions? To advance this challenge, this paper e…
Question the Questions: Auditing Representation in Online Deliberative Processes
Soham De, Lodewijk Gelauff, Ashish Goel +3
A central feature of many deliberative processes, such as citizens' assemblies and deliberative polls, is the opportunity for participants to engage directly with experts. While pa…
CTRL-Rec: Controlling Recommender Systems With Natural Language
Micah Carroll, Adeline Foote, Kevin Feng +4
When users are dissatisfied with recommendations from a recommender system, they often lack fine-grained controls for changing them. Large language models (LLMs) offer a solution b…