in the form of Revised-Prompt, as illustrated
in Figure 3. The LLMs subsequently generate
rationales iteratively using such prompts. As
expected, we observe that the performance of
the LLMs is further improved (increasing from
4089(54.7%) to 4898(59.1%) after the ﬁrst it-
eration and continuing to improve with subse-
quent iterations, ultimately reaching a peak of
5726(76.6%), as shown in Figure 2). This ob-
servation suggests that LLMs can autonomously
rectify errors with the assistance of weak bootstrapping.
Based on the observation, we introduce Iter-CoT (Iterative bootstrapping in Chain-of-Thoughts
Prompting), a novel approach that leverages the question and the rationales during each iteration
of bootstrapping, enabling the model to reﬂect on its errors. This is different from other previous
approaches: our method uses richer contextual information to prompt the LLMs to produce more
precise and comprehensive reasoning chains in the demonstration.
Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models