11 citations · 16 across the 7 of their papers we have counts for
3 papers · 1 filter
Evaluating Frontier Models for Dangerous Capabilities
Mary Phuong, Matthew Aitchison, Elliot Catt +24
To understand the risks posed by a new AI system, we must understand what it can and cannot do. Building on prior work, we introduce a programme of new "dangerous capability" evalu…
Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models
Amal Rannen-Triki, Jorg Bornschein, Razvan Pascanu +5
We consider the problem of online fine tuning the parameters of a language model at test time, also known as dynamic evaluation. While it is generally known that this approach impr…
Learning Universal Predictors
Jordi Grau-Moya, Tim Genewein, Marcus Hutter +8
Meta-learning has emerged as a powerful approach to train neural networks to learn new tasks quickly from limited data. Broad exposure to different tasks leads to versatile represe…