73.2

-

71.1
65.3
63.4
69.3

-

-
-

-
-

-

78.6
67.4

80.0
74.7

-
-

-
-

-

-

40.3

67.8
49.8

Table 1: Accuracy on eleven datasets from arithmetic, commonsense and symbolic reasoning tasks.
The previous ﬁnetuned state-of-the-art models are: α: (Cobbe et al., 2021b); β: (Amini et al., 2019);
γ: (Hosseini et al., 2014); δ: (Hu et al., 2019); (cid:15): (Pi et al., 2022); ζ: (Lan et al., 2022); η: (Xu et al.,
2022). * denotes all three LLMs use Manual-CoT. Iter-CoT(W) and Iter-CoT(S) denote our weak
and strong iterative bootstrapping approaches.

4.3

Implementation Details
Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models