Structure Guided Prompt: Instructing Large Language Model in Multi-Step
  Reasoning by Exploring Graph Structure of the Text
Kewei Cheng Nesreen K. Ahmed Theodore Willke Yizhou Sun
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.