Structure Guided Prompt: Instructing Large Language Model in Multi-Step Reasoning by Exploring Graph Structure of the Text

TL;DR

This paper introduces a graph-based prompting framework that enhances large language models' multi-step reasoning by converting text into graphs and guiding navigation, leading to improved accuracy in complex reasoning tasks.

Contribution

The paper proposes a novel three-stage, task-agnostic prompting framework that explicitly converts text into graphs and guides LLMs to improve multi-step reasoning in a zero-shot setting.

Findings

Significant improvement in reasoning accuracy across multiple NLP tasks.

Effective organization and navigation of graph-structured information.

Enhanced reasoning capabilities in zero-shot scenarios.

Abstract

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.