show that Iter-CoT outperforms existing prompting approaches, and demonstrate that LLMs have the
capability to enhance their performance by integrating iterative bootstrapping for self-correction and
summarization to obtain better reasoning chains for demonstrations.
Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models