Integrating Graphs with Large Language Models: Methods and Prospects
- URL: http://arxiv.org/abs/2310.05499v1
- Date: Mon, 9 Oct 2023 07:59:34 GMT
- Title: Integrating Graphs with Large Language Models: Methods and Prospects
- Authors: Shirui Pan, Yizhen Zheng, Yixin Liu
- Abstract summary: Large language models (LLMs) have emerged as frontrunners, showcasing unparalleled prowess in diverse applications.
Merging the capabilities of LLMs with graph-structured data has been a topic of keen interest.
This paper bifurcates such integrations into two predominant categories.
- Score: 68.37584693537555
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large language models (LLMs) such as GPT-4 have emerged as frontrunners,
showcasing unparalleled prowess in diverse applications, including answering
queries, code generation, and more. Parallelly, graph-structured data, an
intrinsic data type, is pervasive in real-world scenarios. Merging the
capabilities of LLMs with graph-structured data has been a topic of keen
interest. This paper bifurcates such integrations into two predominant
categories. The first leverages LLMs for graph learning, where LLMs can not
only augment existing graph algorithms but also stand as prediction models for
various graph tasks. Conversely, the second category underscores the pivotal
role of graphs in advancing LLMs. Mirroring human cognition, we solve complex
tasks by adopting graphs in either reasoning or collaboration. Integrating with
such structures can significantly boost the performance of LLMs in various
complicated tasks. We also discuss and propose open questions for integrating
LLMs with graph-structured data for the future direction of the field.
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