Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs

TL;DR

This paper introduces Graph-CoT, a framework that enhances large language models by reasoning over interconnected graph data, and presents a new benchmark dataset, GRBench, for evaluating graph-based reasoning in knowledge-intensive tasks.

Contribution

The paper proposes a novel Graph-CoT framework for reasoning on graphs to improve LLM performance and introduces GRBench, a new dataset for benchmarking graph reasoning capabilities.

Findings

Graph-CoT consistently outperforms baseline methods on GRBench.

Systematic experiments validate the effectiveness of graph reasoning in LLMs.

The dataset enables structured evaluation of graph-based reasoning in knowledge tasks.

Abstract

Large language models (LLMs), while exhibiting exceptional performance, suffer from hallucinations, especially on knowledge-intensive tasks. Existing works propose to augment LLMs with individual text units retrieved from external knowledge corpora to alleviate the issue. However, in many domains, texts are interconnected (e.g., academic papers in a bibliographic graph are linked by citations and co-authorships) which form a (text-attributed) graph. The knowledge in such graphs is encoded not only in single texts/nodes but also in their associated connections. To facilitate the research of augmenting LLMs with graphs, we manually construct a Graph Reasoning Benchmark dataset called GRBench, containing 1,740 questions that can be answered with the knowledge from 10 domain graphs. Then, we propose a simple and effective framework called Graph Chain-of-thought (Graph-CoT) to augment LLMs with graphs by encouraging LLMs to reason on the graph iteratively. Each Graph-CoT iteration consists of three sub-steps: LLM reasoning, LLM-graph interaction, and graph execution. We conduct systematic experiments with three LLM backbones on GRBench, where Graph-CoT outperforms the baselines consistently. The code is available at https://github.com/PeterGriffinJin/Graph-CoT.