Related papers: Integrating Graphs with Large Language Models: Methods and Prospects

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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