6

source (examples in this dataset already included reasoning steps like a chain of thought).2 Fig-
ure 6 shows that these prompts performed comparably with our manually written exemplars, also
substantially outperforming standard prompting.
In addition to robustness to annotators, independently-written chains of thought, different exemplars,
and various language models, we also ﬁnd that chain-of-thought prompting for arithmetic reasoning
is robust to different exemplar orders and varying numbers of exemplars (see Appendix A.2).

4 Commonsense Reasoning
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models