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
this https URL

介绍了一种名为 “重述和回答”（RaR）的方法，通过使人类的问题被大型语言模型（LLMs）重述和展开，从而在单个提示中提供回答，以提高 LLM 的性能。还介绍了 RaR 的两步变体方法，通过将问题由一个 LLM 重述后传递给另一个 LLM，有效地利用了由一个 LLM 生成的重述问题。实验证明这些方法显著提高了不同模型在各种任务上的性能，并与 Chain-of-Thought（CoT）方法进行了综合比较，发现 RaR 与 CoT 互补，结合使用效果更好。该工作不仅对提高 LLM 性能具有高效和有效的贡献，还对公正评估 LLM 能力具有重要意义。