  With the help of Chain-of-Thought (CoT) prompting, Large Language Models
(LLMs) have achieved remarkable performance on various reasoning tasks.
However, most of them have been evaluated under noise-free context and the
dilemma for LLMs to produce inaccurate results under the noisy context has not
been fully investigated. Existing studies utilize trigger sentences to
encourage LLMs to concentrate on the relevant information but the trigger has
limited effect on final answer prediction. Inspired by interactive CoT method,
where intermediate reasoning steps are promoted by multiple rounds of
interaction between users and LLMs, we propose a novel prompting method, namely
R$^3$ prompting, for CoT reasoning under noisy context. Specifically, R$^3$
prompting interacts with LLMs to perform key sentence extraction, variable
declaration and answer prediction, which corresponds to a thought process of
reviewing, rephrasing and resolving. The responses generated at the last
interaction will perform as hints to guide toward the responses of the next
interaction. Our experiments show that R$^3$ prompting significantly
outperforms existing CoT prompting methods on five reasoning tasks under noisy
context. With GPT-3.5-turbo, we observe 3.7% accuracy improvement on average on
the reasoning tasks under noisy context compared to the most competitive
prompting baseline. More analyses and ablation studies show the robustness and
generalization of R$^3$ prompting method in solving reasoning tasks in LLMs
under noisy context.
