Reasoning with Graphs: Structuring Implicit Knowledge to Enhance LLMs Reasoning

TL;DR

This paper introduces a method to construct explicit graphs from text context to improve large language models' reasoning abilities, especially in multi-step and multi-hop tasks.

Contribution

The paper proposes Reasoning with Graphs (RwG), a novel approach that structures implicit knowledge into graphs to enhance LLM reasoning without pre-existing graph data.

Findings

Improves logical reasoning accuracy

Enhances multi-hop question answering performance

Effective across multiple reasoning tasks

Abstract

Large language models (LLMs) have demonstrated remarkable success across a wide range of tasks; however, they still encounter challenges in reasoning tasks that require understanding and inferring relationships between distinct pieces of information within text sequences. This challenge is particularly pronounced in tasks involving multi-step processes, such as logical reasoning and multi-hop question answering, where understanding implicit relationships between entities and leveraging multi-hop connections in the given context are crucial. Graphs, as fundamental data structures, explicitly represent pairwise relationships between entities, thereby offering the potential to enhance LLMs' reasoning capabilities. External graphs have proven effective in supporting LLMs across multiple tasks. However, in many reasoning tasks, no pre-existing graph structure is provided. Can we structure implicit knowledge derived from context into graphs to assist LLMs in reasoning? In this paper, we propose Reasoning with Graphs (RwG) by first constructing explicit graphs from the context and then leveraging these graphs to enhance LLM reasoning performance on reasoning tasks. Extensive experiments demonstrate the effectiveness of the proposed method in improving both logical reasoning and multi-hop question answering tasks.