Rephrase and Respond: Let Large Language Models Ask Better Questions for
  Themselves
Yihe Deng Weitong Zhang Zixiang Chen Quanquan Gu
Misunderstandings arise not only in interpersonal communication but also
between humans and Large Language Models (LLMs). Such discrepancies can make
LLMs interpret seemingly unambiguous questions in unexpected ways, yielding
incorrect responses. While it is widely acknowledged that the quality of a
prompt, such as a question, significantly impacts the quality of the response
provided by LLMs, a systematic method for crafting questions that LLMs can
better comprehend is still underdeveloped. In this paper, we present a method
named `Rephrase and Respond' (RaR), which allows LLMs to rephrase and expand
questions posed by humans and provide responses in a single prompt. This
approach serves as a simple yet effective prompting method for improving
performance. We also introduce a two-step variant of RaR, where a rephrasing
LLM first rephrases the question and then passes the original and rephrased
questions together to a different responding LLM. This facilitates the
effective utilization of rephrased questions generated by one LLM with another.
Our experiments demonstrate that our methods significantly improve the
performance of different models across a wide range to tasks. We further
provide a comprehensive comparison between RaR and the popular Chain-of-Thought
(CoT) methods, both theoretically and empirically. We show that RaR is
complementary to CoT and can be combined with CoT to achieve even better
performance. Our work not only contributes to enhancing LLM performance
efficiently and effectively but also sheds light on a fair evaluation of LLM
capabilities. Data and codes are available at
https://github.com/uclaml/Rephrase-and-Respond.