The observed beneﬁts of using chain-of-thought prompting raises the natural question of whether the
same performance improvements can be conferred via other types of prompting. Figure 5 shows an
ablation study with three variations of chain of thought described below.
Equation only. One reason for why chain-of-thought prompting might help is that it produces the
mathematical equation to be evaluated, and so we test a variation where the model is prompted
to output only a mathematical equation before giving the answer. Figure 5 shows that equation
only prompting does not help much for GSM8K, which implies that the semantics of the questions
in GSM8K are too challenging to directly translate into an equation without the natural language
reasoning steps in chain of thought. For datasets of one-step or two-step problems, however, we ﬁnd
that equation only prompting does improve performance, since the equation can be easily derived
from the question (see Appendix Table 6).

5
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models