  We introduce Reprompting, an iterative sampling algorithm that automatically
learns the Chain-of-Thought (CoT) recipes for a given task without human
intervention. Through Gibbs sampling, Reprompting infers the CoT recipes that
work consistently well for a set of training samples by iteratively sampling
new recipes using previously sampled recipes as parent prompts to solve other
training problems. We conduct extensive experiments on 20 challenging reasoning
tasks. Results show that Reprompting outperforms human-written CoT prompts
substantially by +9.4 points on average. It also achieves consistently better
performance than the state-of-the-art prompt optimization and decoding
algorithms.
