Although Large Language Models (LLMs) excel at addressing straightforward
reasoning tasks, they frequently struggle with difficulties when confronted by
more complex multi-step reasoning due to a range of factors. Firstly, natural
language often encompasses complex relationships among entities, making it
challenging to maintain a clear reasoning chain over longer spans. Secondly,
the abundance of linguistic diversity means that the same entities and
relationships can be expressed using different terminologies and structures,
complicating the task of identifying and establishing connections between
multiple pieces of information. Graphs provide an effective solution to
represent data rich in relational information and capture long-term
dependencies among entities. To harness the potential of graphs, our paper
introduces Structure Guided Prompt, an innovative three-stage task-agnostic
prompting framework designed to improve the multi-step reasoning capabilities
of LLMs in a zero-shot setting. This framework explicitly converts unstructured
text into a graph via LLMs and instructs them to navigate this graph using
task-specific strategies to formulate responses. By effectively organizing
information and guiding navigation, it enables LLMs to provide more accurate
and context-aware responses. Our experiments show that this framework
significantly enhances the reasoning capabilities of LLMs, enabling them to
excel in a broader spectrum of natural language scenarios.

我们的论文引入了结构引导提示，这是一个创新的三阶段任务无关提示框架，旨在提高大型语言模型在零样本环境下的多步推理能力。通过将非结构化文本转换为图形，指导模型在图形中导航，并使用任务特定的策略来制定响应，我们的实验表明这一框架显著增强了大型语言模型的推理能力，使其在更广泛的自然语言场景中表现出色。