The NLP landscape has recently been revolutionized by
language models (Peters et al., 2018; Devlin et al., 2019;
Brown et al., 2020, inter alia). Scaling up the size of lan-
guage models has been shown to confer a range of beneﬁts,
such as improved performance and sample efﬁciency (Ka-
plan et al., 2020; Brown et al., 2020, inter alia). However,
scaling up model size alone has not proved sufﬁcient for
achieving high performance on challenging tasks such as
arithmetic, commonsense, and symbolic reasoning (Rae
et al., 2021).
This work explores how the reasoning ability of large
language models can be unlocked by a simple method
motivated by two ideas. First, techniques for arithmetic
reasoning can beneﬁt from generating natural language
rationales that lead to the ﬁnal answer. Prior work has
given models the ability to generate natural language inter-
mediate steps by training from scratch (Ling et al., 2017)
or ﬁnetuning a pretrained model (Cobbe et al., 2021), in
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models