type of manual annotation, where humans only need to annotate the incorrect reasoning chains, rather
than requiring design the demonstrations from scratch.
We conduct an additional experiment to generate CoT and Iter-CoT by randomly selecting examples
from the whole train set from GSM8K. For Iter-CoT, we employ the strong bootstrapping approach
for incorrect questions where weak bootstrapping is insufﬁcient due to overly difﬁcult questions.
Speciﬁcally, we directly summarize the reasoning chain for initially correct questions. Our results
indicate that Iter-CoT can effectively improve randomly sampled demonstrations performance;
however, the extent of improvement is limited (approximately 1.8%). The results also conﬁrm
that Iter-CoT has a primary enhancement effect on selecting examples with appropriate difﬁculty
levels. Therefore, Iter-CoT can effectively enhance performance by ensuring the selection of the most
appropriate exemplars.
Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models