307 citations · 371 across the 6 of their papers we have counts for
6 papers
Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training
Evan Hubinger, Carson Denison, Jesse Mu +36
Humans are capable of strategically deceptive behavior: behaving helpfully in most situations, but then behaving very differently in order to pursue alternative objectives when giv…
Managing extreme AI risks amid rapid progress
Yoshua Bengio, Geoffrey Hinton, Andrew Yao +22
Artificial Intelligence (AI) is progressing rapidly, and companies are shifting their focus to developing generalist AI systems that can autonomously act and pursue goals. Increase…
How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions
Lorenzo Pacchiardi, Alex J. Chan, Sören Mindermann +5
Large language models (LLMs) can "lie", which we define as outputting false statements despite "knowing" the truth in a demonstrable sense. LLMs might "lie", for example, when inst…
DeDUCE: Generating Counterfactual Explanations Efficiently
Benedikt Höltgen, Lisa Schut, Jan M. Brauner +1
When an image classifier outputs a wrong class label, it can be helpful to see what changes in the image would lead to a correct classification. This is the aim of algorithms gener…
Prioritized training on points that are learnable, worth learning, and not yet learned (workshop version)
Sören Mindermann, Muhammed Razzak, Winnie Xu +7
We introduce Goldilocks Selection, a technique for faster model training which selects a sequence of training points that are "just right". We propose an information-theoretic acqu…
How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19?
Mrinank Sharma, Sören Mindermann, Jan Markus Brauner +7
To what extent are effectiveness estimates of nonpharmaceutical interventions (NPIs) against COVID-19 influenced by the assumptions our models make? To answer this question, we inv…