8 citations · 27 across the 15 of their papers we have counts for
5 papers · 1 filter
When Models Don't Manipulate Manifolds: The Geometry of a Comparison Task
Sai Sumedh R. Hindupur, Hadas Orgad, Thomas Fel +1
One of the current premises of mechanistic interpretability research is that detailed accounts of the geometry of neural network representations can tell us how models perform comp…
Interpretability Can Be Actionable
Hadas Orgad, Fazl Barez, Tal Haklay +9
Interpretability aims to explain the behavior of deep neural networks. Despite rapid growth, there is mounting concern that much of this work has not translated into practical impa…
A Robust Evaluation of Probe Robustness: Lessons for Reliable OOD Uncertainty Quantification
Joe Stacey, Hadas Orgad, Kentaro Inui +2
Recent work has shown that the hidden states of large language models contain signals useful for uncertainty estimation, motivating a growing interest in efficient probe-based appr…
Large Language Models Generate Harmful Responses Using a Distinct Mechanism, Shared Across Harm Types
Hadas Orgad, Boyi Wei, Kaden Zheng +4
Large language models remain vulnerable to jailbreaks that elicit harmful responses, yet the mechanism behind harmful response generation is poorly understood. Here, we investigate…
Agents of Chaos
Natalie Shapira, Chris Wendler, Avery Yen +35
We report an exploratory red-teaming study of autonomous language-model-powered agents deployed in a live laboratory environment with persistent memory, email accounts, Discord acc…