Variable compute only. Another intuition is that chain of
thought allows the model to spend more computation (i.e.,
intermediate tokens) on harder problems. To isolate the effect
of variable computation from chain-of-thought reasoning, we
test a conﬁguration where the model is prompted to output a
only sequence of dots (. . .) equal to the number of characters in
the equation needed to solve the problem. This variant performs
about the same as the baseline, which suggests that variable
computation by itself is not the reason for the success of chain-
of-thought prompting, and that there appears to be utility from
expressing intermediate steps via natural language.
Chain of thought after answer. Another potential beneﬁt of
chain-of-thought prompting could simply be that such prompts
allow the model to better access relevant knowledge acquired
during pretraining. Therefore, we test an alternative conﬁgura-
tion where the chain of thought prompt is only given after the
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models