Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs
(2404.07103)Abstract
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.
Overview
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The paper introduces Graph Chain-of-thought (Graph-CoT), a novel framework designed to augment LLMs by enabling them to reason with graph-structured knowledge.
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Graph-CoT constitutes a shift from traditional text-based augmentation, focusing on leveraging the interconnected structure of knowledge within graphs to enhance LLM reasoning skills.
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A new dataset, Graph Reasoning Benchmark (GRBench), comprising 1,740 questions across ten domains, is introduced to evaluate the performance of graph-augmented LLMs.
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Experimental findings show that Graph-CoT significantly outperforms baseline methodologies, including standard LLMs and both text and graph retrieval-augmented LLMs, underscoring its potential to improve LLM reasoning capabilities.
Enhancing LLMs with Graph Reasoning Capabilities: Introducing Graph-CoT and the GRBench Dataset
Introduction to Graph-CoT
The integration of external knowledge sources with LLMs is a burgeoning area of research aiming to mitigate the hallucination issues inherent in these models. Traditional approaches have focused on augmenting LLMs with external text corpora as knowledge bases. However, such methods often overlook the rich, interconnected structure of knowledge as represented in graphs. This research introduces a novel framework, Graph Chain-of-thought (Graph-CoT), which augments LLMs with graph-structured knowledge, facilitating iterative reasoning over interconnected data. This paper also presents the Graph Reasoning Benchmark dataset (GRBench), containing 1,740 questions that leverage knowledge from ten varied domain graphs for evaluation.
The Graph-CoT Framework
Graph-CoT represents a strategic departure from conventional text-based augmentation, emphasizing the iterative exploration of graph structures to enhance LLM reasoning capabilities. The framework comprises three principal components: LLM reasoning, LLM-graph interaction, and graph execution. This meticulous process enables LLMs to pinpoint and extract relevant information from the graph iteratively, substantially improving the model's ability to derive accurate conclusions from complex, structured knowledge sources.
Key Contributions and Findings
The research articulates several pivotal contributions:
- The introduction of GRBench, a comprehensive dataset designed specifically for evaluating the efficacy of graph-augmented LLMs across five distinct domains: academic, e-commerce, literature, healthcare, and legal.
- The development of the Graph-CoT framework, which significantly advances the capacity of LLMs to iteratively reason with graph-structured knowledge sources.
- Comprehensive experimental validation demonstrating the superior performance of Graph-CoT over several baseline methodologies, including standard LLMs, text retrieval-augmented LLMs (Text RAG), and graph retrieval-augmented LLMs (Graph RAG).
The findings consistently illustrate that Graph-CoT outperforms baseline approaches across all domains included in the GRBench dataset. These results underscore the framework's potential to significantly enhance the reasoning capabilities of LLMs by leveraging the intrinsic structure of graph-based knowledge.
Implications and Future Directions
The implications of this research are multifold, spanning both theoretical advancements and practical applications. Theoretically, it highlights the limitations of existing LLM augmentation methodologies that fail to consider the structured nature of knowledge. Practically, it establishes a viable pathway towards leveraging graph-based external knowledge sources, opening new vistas in domains where knowledge is inherently interconnected.
Future research trajectories may explore several avenues, including the development of more sophisticated methods for graph understanding by LLMs and the enhancement of reasoning paradigms to accommodate more complex graph structures. Additionally, expanding the GRBench dataset or integrating dynamic graph knowledge sources could further refine and extend the framework's applicability and performance.
Conclusion
This research marks a significant step forward in the augmentation of LLMs with graph-structured external knowledge. By introducing the Graph-CoT framework and the GRBench benchmark dataset, it lays the groundwork for a new era of LLM capabilities, enabling more accurate, reliable, and nuanced reasoning across a broad spectrum of domains. The findings not only underscore the limitations of existing approaches but also pave the way for future innovations in LLM augmentation and reasoning methodologies.
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