243 citations · 254 across the 3 of their papers we have counts for
12 papers
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.…
Challenges in Detoxifying Language Models
Johannes Welbl, Amelia Glaese, Jonathan Uesato +7
Large language models (LM) generate remarkably fluent text and can be efficiently adapted across NLP tasks. Measuring and guaranteeing the quality of generated text in terms of saf…
Evaluating the Apperception Engine
Richard Evans, Jose Hernandez-Orallo, Johannes Welbl +2
The Apperception Engine is an unsupervised learning system. Given a sequence of sensory inputs, it constructs a symbolic causal theory that both explains the sensory sequence and a…
Undersensitivity in Neural Reading Comprehension
Johannes Welbl, Pasquale Minervini, Max Bartolo +2
Current reading comprehension models generalise well to in-distribution test sets, yet perform poorly on adversarially selected inputs. Most prior work on adversarial inputs studie…
Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension
Max Bartolo, Alastair Roberts, Johannes Welbl +2
Innovations in annotation methodology have been a catalyst for Reading Comprehension (RC) datasets and models. One recent trend to challenge current RC models is to involve a model…
Reducing Sentiment Bias in Language Models via Counterfactual Evaluation
Po-Sen Huang, Huan Zhang, Ray Jiang +6
Advances in language modeling architectures and the availability of large text corpora have driven progress in automatic text generation. While this results in models capable of ge…