73 citations · 320 across the 16 of their papers we have counts for
8 papers · 1 filter
CLAM: Selective Clarification for Ambiguous Questions with Generative Language Models
Lorenz Kuhn, Yarin Gal, Sebastian Farquhar
Users often ask dialogue systems ambiguous questions that require clarification. We show that current language models rarely ask users to clarify ambiguous questions and instead pr…
Understanding Approximation for Bayesian Inference in Neural Networks
Sebastian Farquhar
Bayesian inference has theoretical attractions as a principled framework for reasoning about beliefs. However, the motivations of Bayesian inference which claim it to be the only '…
Do Bayesian Neural Networks Need To Be Fully Stochastic?
Mrinank Sharma, Sebastian Farquhar, Eric Nalisnick +1
We investigate the benefit of treating all the parameters in a Bayesian neural network stochastically and find compelling theoretical and empirical evidence that this standard cons…
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…
Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt
Sören Mindermann, Jan Brauner, Muhammed Razzak +8
Training on web-scale data can take months. But most computation and time is wasted on redundant and noisy points that are already learnt or not learnable. To accelerate training,…
Path-Specific Objectives for Safer Agent Incentives
Sebastian Farquhar, Ryan Carey, Tom Everitt
We present a general framework for training safe agents whose naive incentives are unsafe. As an example, manipulative or deceptive behaviour can improve rewards but should be avoi…