activity
20192022
most citedScaling Language Models: Methods, Analysis & Insights from Training Gopher

243 citations · 412 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2022133 cited

Improving alignment of dialogue agents via targeted human judgements

Amelia Glaese, Nat McAleese, Maja Trębacz +31

We present Sparrow, an information-seeking dialogue agent trained to be more helpful, correct, and harmless compared to prompted language model baselines. We use reinforcement lear…

cs.CL2022243 cited

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.…

cs.CL2021

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…

cs.CL202112 cited

Decoupling the Role of Data, Attention, and Losses in Multimodal Transformers

Lisa Anne Hendricks, John Mellor, Rosalia Schneider +2

Recently multimodal transformer models have gained popularity because their performance on language and vision tasks suggest they learn rich visual-linguistic representations. Focu…

cs.CV201924 cited

Unsupervised Doodling and Painting with Improved SPIRAL

John F. J. Mellor, Eunbyung Park, Yaroslav Ganin +7

We investigate using reinforcement learning agents as generative models of images (extending arXiv:1804.01118). A generative agent controls a simulated painting environment, and is…