high-quality reasoning chains can signiﬁcantly improve LLMs’ reasoning performance. Moreover,
we introduce a new criterion for assessing the difﬁculty level of questions based on whether they can
be easily solved by LLMs. Analysis indicates that the hop-based criterion and ours yield comparable
yet distinguishable results, as shown in Appendix A.4.
We evaluate Iter-CoT on three distinct reasoning tasks (arithmetic, commonsense and symbolic) across
eleven datasets (GSM8K (Cobbe et al., 2021a), AQuA (Ling et al., 2017), AddSub (Hosseini et al.,
2014), SingleEq (Koncel-Kedziorski et al., 2015), SVAMP (Patel et al., 2021), ASDiV (Miao et al.,
2020), CSQA (Talmor et al., 2019), StrategyQA (Geva et al., 2021), Date Understanding, Last Letter
Concatenation (Wei et al., 2022) and Object Tracking (Kojima et al., 2022)). The experimental results
show that Iter-CoT outperforms existing prompting approaches, and demonstrate that LLMs have the
Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models