3.2 Results

The strongest results of chain-of-thought prompting are summarized in Figure 4, with all experimental
outputs for each model collection, model size, and benchmark shown in Table 2 in the Appendix.
There are three key takeaways. First, Figure 4 shows that chain-of-thought prompting is an emergent
ability of model scale (Wei et al., 2022b). That is, chain-of-thought prompting does not positively
impact performance for small models, and only yields performance gains when used with models of
∼100B parameters. We qualitatively found that models of smaller scale produced ﬂuent but illogical
chains of thought, leading to lower performance than standard prompting.

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