Authors: Qiheng Mao, Zemin Liu, Chenghao Liu, Zhuo Li, Jianling Sun

Published on: February 04, 2024

Impact Score: 8.22

Arxiv code: Arxiv:2402.05952

Summary

• What is new: The survey introduces a novel taxonomy for combining Large Language Models (LLMs) with Graph Representation Learning (GRL), addressing the lack of a comprehensive review in this area.
• Why this is important: There’s a gap in research for a detailed analysis of integrating LLMs with GRL for improving data structure analysis.
• What the research proposes: The paper proposes a new taxonomy breaking down models into components and operation techniques, offering insights into effective design and training.
• Results: The new taxonomy facilitates a better understanding of integrating LLMs and GRL, highlighting effective strategies and suggesting areas for future research.

Technical Details

Technological frameworks used: Large Language Models, Graph Representation Learning

Models used: Knowledge extractors and organizers, Integration and training strategies

Data used: Not specified

Potential Impact

Tech companies focused on AI and data analysis, companies in sectors requiring complex data structure analysis such as finance, healthcare, and social networks.

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