Reprompting: Automated Chain-of-Thought Prompt Inference Through Gibbs
  Sampling
Weijia Xu Andrzej Banburski-Fahey Nebojsa Jojic
We introduce Reprompting, an iterative sampling algorithm that searches for
the Chain-of-Thought (CoT) recipes for a given task without human intervention.
Through Gibbs sampling, we infer CoT recipes that work consistently well for a
set of training samples. Our method iteratively samples new recipes using
previously sampled solutions as parent prompts to solve other training
problems. On five Big-Bench Hard tasks that require multi-step reasoning,
Reprompting achieves consistently better performance than the zero-shot,
few-shot, and human-written CoT baselines. Reprompting can also facilitate
transfer of knowledge from a stronger model to a weaker model leading to
substantially improved performance of the weaker model. Overall, Reprompting
brings up to +17 point improvements over the previous state-of-the-art method
that uses human-written CoT prompts.