Chain of Thought prompting strategy has enhanced the performance of Large
Language Models (LLMs) across various NLP tasks. However, it still has
shortcomings when dealing with complex reasoning tasks,
following~\citet{cot_wei}, including understanding errors, calculation errors
and process errors (e.g. missing-step and hallucinations). Subsequently, Our
in-depth analysis of various error types has found that deeply understanding
the whole problem is critical in addressing complicated reasoning tasks. In
this paper, we proposed a novel prompt strategy called Deeply Understanding the
Problems (DUP) prompting, inspired by how humans solve complex reasoning
problems, designed to enhance the comprehensive understanding of problems by
LLMs. It consists of three stages: 1) extract the core question; 2) find out
problem-solving information based on the core question; 3) generate and extract
answers by LLMs. We evaluate the performance of DUP prompting on ten diverse
reasoning datasets. Experimental results suggest that DUP prompting
significantly outperforms Zero-Shot CoT ~\cite{kojima2022large} across all
datasets. Notably, DUP achieves \textbf{state-of-the-art on SVAMP (90.4\% to
94.2\%) and GSM8K (94.6\% to 97.1\%).}

我们提出了一种名为深度理解问题（DUP）提示的新型提示策略，用于增强 LLMs 对问题的全面理解。实验结果表明，DUP 提示在各个数据集上明显优于零 - shot CoT，并在 SVAMP（90.4% 至 94.2%）和 GSM8K（94.6% 至 97.1%）方面取得了最新的成果。