Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans,
Olivier Bousquet, Quoc Le, and Ed H. Chi. Least-to-most prompting enables complex reasoning
in large language models. arXiv Preprint, 2022. doi: 10.48550/arXiv.2205.10625. URL https:
//doi.org/10.48550/arXiv.2205.10625.

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A Analysis for Iter-CoT

A.1 Effective of Different Numbers of Seed Examples

In order to investigate the sensitivity of our approaches and conventional CoT to the seed examples,
we conducted an experiment on the GSM8K dataset as shown in Figure 5. It demonstrates that
both of our approaches outperform CoT, and are more stable as the number of examples increases.
Additionally, our experiment also shows that the overall performance is not determined by the quantity
of increasing exemplars. For instance, the Iter-CoT(W) peak occurs at ﬁve exemplars, while the
Iter-CoT(S) and Random-CoT peaks at four exemplars.
Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models