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. We first perform a
re-organization processing of the contextual content, e.g., documents or
paragraphs, to obtain logical relationships. 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. 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 improvement of 3\% across all tasks,
highlighting its potential to improve the reasoning performance of LLMs. Our
source code is available at this https URL

提出了一种信息重新组织（InfoRE）方法，通过在推理之前对上下文内容进行重新组织处理，从而增强了大型语言模型（LLMs）的推理能力。在各种上下文感知的多跳推理任务中，通过零射击设置，我们实验评估了该方法在 Llama2-70B、GPT-3.5 和 GPT-4 上的效果，平均改进了 3％，突显了它改善 LLMs 推理性能的潜力。