95 citations · 221 across the 15 of their papers we have counts for
5 papers · 1 filter
Cognitive Dissonance: Why Do Language Model Outputs Disagree with Internal Representations of Truthfulness?
Kevin Liu, Stephen Casper, Dylan Hadfield-Menell +1
Neural language models (LMs) can be used to evaluate the truth of factual statements in two ways: they can be either queried for statement probabilities, or probed for internal rep…
Measuring the Success of Diffusion Models at Imitating Human Artists
Stephen Casper, Zifan Guo, Shreya Mogulothu +5
Modern diffusion models have set the state-of-the-art in AI image generation. Their success is due, in part, to training on Internet-scale data which often includes copyrighted wor…
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Stephen Casper, Xander Davies, Claudia Shi +29
Reinforcement learning from human feedback (RLHF) is a technique for training AI systems to align with human goals. RLHF has emerged as the central method used to finetune state-of…
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…
Red Teaming Deep Neural Networks with Feature Synthesis Tools
Stephen Casper, Yuxiao Li, Jiawei Li +4
Interpretable AI tools are often motivated by the goal of understanding model behavior in out-of-distribution (OOD) contexts. Despite the attention this area of study receives, the…