activity
20172026
most citedInverse Reward Design

61 citations · 252 across the 19 of their papers we have counts for

collaborators

23 papers

cs.GT2026

Social Choice Foundations for Simulation-Augmented Generation

Sonja Kraiczy, Smitha Milli, Ratip Emin Berker +6

Simulation-augmented generation (SAGE) is a recent technical proposal in which models simulate individuals' viewpoints at inference time in order to provide more representative ans…

cs.CY2026

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…

cs.SI2026★ 1 cited

The Prosocial Ranking Challenge: Reducing Polarization on Social Media without Sacrificing Engagement

Jonathan Stray, Ian Baker, George Beknazar-Yuzbashev +41

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…

cs.AI2025

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…

cs.AI2025

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…

cs.CL2025

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…