  Language modelling provides a step towards intelligent communication systems
by harnessing large repositories of written human knowledge to better predict
and understand the world. In this paper, we present an analysis of
Transformer-based language model performance across a wide range of model
scales -- from models with tens of millions of parameters up to a 280 billion
parameter model called Gopher. These models are evaluated on 152 diverse tasks,
achieving state-of-the-art performance across the majority. Gains from scale
are largest in areas such as reading comprehension, fact-checking, and the
identification of toxic language, but logical and mathematical reasoning see
less benefit. We provide a holistic analysis of the training dataset and
model's behaviour, covering the intersection of model scale with bias and
toxicity. Finally we discuss the application of language models to AI safety
and the mitigation of downstream harms.
