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Found 8 papers, 1 papers with code
Teaching language models to support answers with verified quotes
We measure the performance of GopherCite by conducting human evaluation of answers to questions in a subset of the NaturalQuestions and ELI5 datasets.
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
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Alignment of Language Agents
For artificial intelligence to be beneficial to humans the behaviour of AI agents needs to be aligned with what humans want.
Causal Analysis of Agent Behavior for AI Safety
As machine learning systems become more powerful they also become increasingly unpredictable and opaque.
Algorithms for Causal Reasoning in Probability Trees
Probability trees are one of the simplest models of causal generative processes.
Meta-trained agents implement Bayes-optimal agents
Memory-based meta-learning is a powerful technique to build agents that adapt fast to any task within a target distribution.
Neural networks are a priori biased towards Boolean functions with low entropy
Understanding the inductive bias of neural networks is critical to explaining their ability to generalise.
Risks from Learned Optimization in Advanced Machine Learning Systems
We analyze the type of learned optimization that occurs when a learned model (such as a neural network) is itself an optimizer - a situation we refer to as mesa-optimization, a neologism we introduce in this paper.
