Improving the reasoning capabilities of large language models (LLMs) has attracted considerable interest. Recent approaches primarily focus on improving the reasoning process to yield a more precise final answer. However, in scenarios involving contextually aware reasoning, these methods neglect the importance of first identifying logical relationships from the context before proceeding with the reasoning. This oversight could lead to a superficial understanding and interaction with the context, potentially undermining the quality and reliability of the reasoning outcomes. In this paper, we propose an information re-organization (InfoRE) method before proceeding with the reasoning to enhance the reasoning ability of LLMs. Our re-organization method involves initially extracting logical relationships from the contextual content, such as documents or paragraphs, and subsequently pruning redundant content to minimize noise. Then, we utilize the re-organized information in the reasoning process. This enables LLMs to deeply understand the contextual content by clearly perceiving these logical relationships, while also ensuring high-quality responses by eliminating potential noise. To demonstrate the effectiveness of our approach in improving the reasoning ability, we conduct experiments using Llama2-70B, GPT-3.5, and GPT-4 on various contextually aware multi-hop reasoning tasks. Using only a zero-shot setting, our method achieves an average absolute improvement of 4% across all tasks, highlighting its potential to improve the reasoning performance of LLMs. Our source code is available at https://github.com/hustcxx/InfoRE.

翻译：提升大型语言模型（LLMs）的推理能力已引起广泛关注。现有方法主要聚焦于优化推理过程以获得更精确的最终答案。然而，在涉及情境感知推理的场景中，这些方法忽视了在展开推理前首先从上下文中识别逻辑关系的重要性。这种疏忽可能导致对上下文的理解和交互流于表面，进而损害推理结果的质量与可靠性。本文提出一种在推理前进行信息重组（InfoRE）的方法，以增强LLMs的推理能力。我们的重组方法首先从文档或段落等上下文内容中提取逻辑关系，随后剪枝冗余内容以降低噪声干扰，最后将重组后的信息用于推理过程。该方法使LLMs能够通过清晰感知这些逻辑关系来深入理解上下文内容，同时通过消除潜在噪声确保高质量的回答。为验证本方法在提升推理能力方面的有效性，我们使用Llama2-70B、GPT-3.5和GPT-4在多种情境感知多跳推理任务上进行了实验。在零样本设定下，我们的方法在所有任务中平均实现了4%的绝对性能提升，彰显了其改善LLMs推理性能的潜力。源代码发布于https://github.com/hustcxx/InfoRE。