9.7k citations · 11.2k across the 18 of their papers we have counts for
6 papers · 2 filters
Fine-Tuning Language Models via Epistemic Neural Networks
Ian Osband, Seyed Mohammad Asghari, Benjamin Van Roy +3
Language models often pre-train on large unsupervised text corpora, then fine-tune on additional task-specific data. However, typical fine-tuning schemes do not prioritize the exam…
Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Models
Maribeth Rauh, John Mellor, Jonathan Uesato +9
Large language models produce human-like text that drive a growing number of applications. However, recent literature and, increasingly, real world observations, have demonstrated…
Teaching language models to support answers with verified quotes
Jacob Menick, Maja Trebacz, Vladimir Mikulik +8
Recent large language models often answer factual questions correctly. But users can't trust any given claim a model makes without fact-checking, because language models can halluc…
Uncertainty Estimation for Language Reward Models
Adam Gleave, Geoffrey Irving
Language models can learn a range of capabilities from unsupervised training on text corpora. However, to solve a particular problem (such as text summarization) it is typically ne…
Red Teaming Language Models with Language Models
Ethan Perez, Saffron Huang, Francis Song +6
Language Models (LMs) often cannot be deployed because of their potential to harm users in hard-to-predict ways. Prior work identifies harmful behaviors before deployment by using…
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Jack W. Rae, Sebastian Borgeaud, Trevor Cai +77
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.…