Reasoning presents a significant and challenging issue for Large Language Models (LLMs). The predominant focus of research has revolved around developing diverse prompting strategies to guide and structure the reasoning processes of LLMs. However, these approaches based on decoder-only causal language models often operate the input question in a single forward pass, potentially missing the rich, back-and-forth interactions inherent in human reasoning. Scant attention has been paid to a critical dimension, i.e., the input question itself embedded within the prompts. In response, we introduce a deceptively simple yet highly effective prompting strategy, termed question "re-reading". Drawing inspiration from human learning and problem-solving, re-reading entails revisiting the question information embedded within input prompts. This approach aligns seamlessly with the cognitive principle of reinforcement, enabling LLMs to extract deeper insights, identify intricate patterns, establish more nuanced connections, and ultimately enhance their reasoning capabilities across various tasks. Experiments conducted on a series of reasoning benchmarks serve to underscore the effectiveness and generality of our method. Moreover, our findings demonstrate that our approach seamlessly integrates with various language models, though-eliciting prompting methods, and ensemble techniques, further underscoring its versatility and compatibility in the realm of LLMs.

翻译：推理对于大型语言模型（LLMs）而言是一个重大且具有挑战性的问题。当前研究的主要焦点一直围绕开发多样化的提示策略，以引导和结构化LLMs的推理过程。然而，这些基于仅解码器因果语言模型的方法通常对输入问题执行单次前向传递，可能忽略了人类推理中固有的丰富交互反馈过程。研究者很少关注一个关键维度，即提示中嵌入的输入问题本身。为此，我们提出一种看似简单却极为有效的提示策略，称为问题“重新阅读”。受人类学习和问题解决过程的启发，重新阅读涉及反复审视输入提示中嵌入的问题信息。这种方法与强化的认知原则无缝契合，使LLMs能够提取更深层次的见解、识别复杂模式、建立更细微的联系，并最终提升其在各类任务中的推理能力。在一系列推理基准测试上进行的实验充分证明了我们方法的有效性和通用性。此外，我们的研究结果表明，该方法能够与多种语言模型、提示激发方法及集成技术无缝集成，进一步凸显了其在LLMs领域的多功能性和兼容性。