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.

本文通过对转换器为基础的语言模型在不同规模下的性能分析，证明规模越大，其在阅读理解、事实检查和鉴别有害语言等方面的性能提高最大，但在逻辑推理和数学推理等方面的收益较少。此外，本文还介绍了训练数据集和模型行为的综合分析，探讨了语言模型在 AI 安全和减轻下游危害中的应用。