activity
20222026
most citedWhen Large Language Models are More PersuasiveThan Incentivized Humans, and Why

7 citations · 23 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CY2026

Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts

Alexander K. Saeri, Jess Graham, Michael Noetel +185

Artificial intelligence poses many risks, ranging from familiar present-day harms to unprecedented and potentially catastrophic ones. Effective risk management requires prioritizat…

cs.CY2026★ 3 cited

Measuring AI R&D Automation

Alan Chan, Ranay Padarath, Joe Kwon +2

The automation of AI R&D (AIRDA) could have significant implications, but its extent and ultimate effects remain uncertain. We need empirical data to resolve these uncertainties, b…

cs.AI2026

Internal Deployment Gaps in AI Regulation

Joe Kwon, Stephen Casper

Frontier AI regulations primarily focus on systems deployed to external users, where deployment is more visible and subject to outside scrutiny. However, high-stakes applications c…

cs.CL2025★ 7 cited

When Large Language Models are More PersuasiveThan Incentivized Humans, and Why

Jiacheng Liu, Francesco Salvi, Philipp Schoenegger +37

Large Language Models (LLMs) have been shown to be highly persuasive, but when and why they outperform humans is still an open question. We compare the persuasiveness of two LLMs (…

cs.LG2024

Comparing Bottom-Up and Top-Down Steering Approaches on In-Context Learning Tasks

Madeline Brumley, Joe Kwon, David Krueger +2

A key objective of interpretability research on large language models (LLMs) is to develop methods for robustly steering models toward desired behaviors. To this end, two distinct…

cs.CL2023★ 7 cited

Explore, Establish, Exploit: Red Teaming Language Models from Scratch

Stephen Casper, Jason Lin, Joe Kwon +2

Deploying large language models (LMs) can pose hazards from harmful outputs such as toxic or false text. Prior work has introduced automated tools that elicit harmful outputs to id…

cs.LG2022★ 6 cited

Forecasting Future World Events with Neural Networks

Andy Zou, Tristan Xiao, Ryan Jia +7

Forecasting future world events is a challenging but valuable task. Forecasts of climate, geopolitical conflict, pandemics and economic indicators help shape policy and decision ma…