Beyond Text: A Deep Dive into Large Language Models' Ability on Understanding Graph Data
This work addresses the gap in evaluating LLMs for graph analytics, which is important for researchers and practitioners in machine learning and data science, though it is incremental as it focuses on benchmarking rather than proposing new methods.
The paper tackled the problem of assessing large language models' (LLMs) ability to understand graph-structured data by benchmarking them on diverse graph prediction tasks, finding that LLMs can process graph data but with limitations compared to specialized graph neural networks.
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.