Beyond Text: A Deep Dive into Large Language Models' Ability on Understanding Graph Data

Published: 28 Oct 2023, Last Modified: 21 Dec 2023NeurIPS 2023 GLFrontiers Workshop PosterEveryoneRevisionsBibTeX
Keywords: Large language model, Graph learning, Graph Neural Networks, Benchmark
TL;DR: The paper assesses large language models' proficiency on graph tasks, comparing them to specialized graph neural networks and highlighting their potential in graph analytics.
Abstract: Large language models (LLMs) have achieved impressive performance on many natural language processing tasks. However, their capabilities on graph-structured data remain relatively unexplored. In this paper, we conduct a series of experiments benchmarking leading LLMs on diverse graph prediction tasks spanning node, edge, and graph levels. We aim to assess whether LLMs can effectively process graph data and leverage topological structures to enhance performance, compared to specialized graph neural networks. Through varied prompt formatting and task/dataset selection, we analyze how well LLMs can interpret and utilize graph structures. By comparing LLMs' performance with specialized graph models, we offer insights into the strengths and limitations of employing LLMs for graph analytics. Our findings provide insights into LLMs' capabilities and suggest avenues for further exploration in applying them to graph analytics.
Submission Number: 82