Beyond Text: A Deep Dive into Large Language Models' Ability on
Understanding Graph Data
- URL: http://arxiv.org/abs/2310.04944v1
- Date: Sat, 7 Oct 2023 23:25:22 GMT
- Title: Beyond Text: A Deep Dive into Large Language Models' Ability on
Understanding Graph Data
- Authors: Yuntong Hu, Zheng Zhang, Liang Zhao
- Abstract summary: Large language models (LLMs) have achieved impressive performance on many natural language processing tasks.
We aim to assess whether LLMs can effectively process graph data and leverage topological structures to enhance performance.
By comparing LLMs' performance with specialized graph models, we offer insights into the strengths and limitations of employing LLMs for graph analytics.
- Score: 13.524529952170672
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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