activity
20222024
most citedSemantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation

52 citations · 80 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI20243 cited

Holistic Safety and Responsibility Evaluations of Advanced AI Models

Laura Weidinger, Joslyn Barnhart, Jenny Brennan +16

Safety and responsibility evaluations of advanced AI models are a critical but developing field of research and practice. In the development of Google DeepMind's advanced AI models…

cs.LG202411 cited

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…

cs.LG20237 cited

Prediction-Oriented Bayesian Active Learning

Freddie Bickford Smith, Andreas Kirsch, Sebastian Farquhar +3

Information-theoretic approaches to active learning have traditionally focused on maximising the information gathered about the model parameters, most commonly by optimising the BA…

cs.CL202352 cited

Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation

Lorenz Kuhn, Yarin Gal, Sebastian Farquhar

We introduce a method to measure uncertainty in large language models. For tasks like question answering, it is essential to know when we can trust the natural language outputs of…

cs.AI20227 cited

Discovering Agents

Zachary Kenton, Ramana Kumar, Sebastian Farquhar +3

Causal models of agents have been used to analyse the safety aspects of machine learning systems. But identifying agents is non-trivial -- often the causal model is just assumed by…