8 citations · 12 across the 4 of their papers we have counts for
5 papers
How AI Writing Assistance Distorts Perceptions of AI Users and Their Opinions
Paul Röttger, Kobi Hackenburg, Hannah Rose Kirk +1
Hundreds of millions of people use AI for writing assistance. We asked how AI distorts perceptions of AI users and their opinions. Writers (N=2,939) wrote political opinion paragra…
Reward Models Inherit Value Biases from Pretraining
Brian Christian, Jessica A. F. Thompson, Elle Michelle Yang +4
Reward models (RMs) are central to aligning large language models (LLMs) with human values but have received less attention than pretrained and post-trained LLMs themselves. Becaus…
Measuring what Matters: Construct Validity in Large Language Model Benchmarks
Andrew M. Bean, Ryan Othniel Kearns, Angelika Romanou +39
Evaluating large language models (LLMs) is crucial for both assessing their capabilities and identifying safety or robustness issues prior to deployment. Reliably measuring abstrac…
Reward Model Interpretability via Optimal and Pessimal Tokens
Brian Christian, Hannah Rose Kirk, Jessica A. F. Thompson +2
Reward modeling has emerged as a crucial component in aligning large language models with human values. Significant attention has focused on using reward models as a means for fine…
Why human-AI relationships need socioaffective alignment
Hannah Rose Kirk, Iason Gabriel, Chris Summerfield +2
Humans strive to design safe AI systems that align with our goals and remain under our control. However, as AI capabilities advance, we face a new challenge: the emergence of deepe…