A Comprehensive Review of Integrating Large Language Models #NLP

Graph Machine Learning (Graph ML) has become essential for modeling complex relationships in various domains like social networks and molecular discovery. With the integration of Large Language Models (LLMs), Graph ML has seen significant advancements, enhancing generalization capabilities through self-supervised learning methods. Researchers aim to provide a comprehensive review of recent advancements in Graph ML, focusing on the evolution from early methods to the latest Graph Foundation Models (GFMs) in the era of LLMs. They analyze current LLM-enhanced Graph ML methods, explore the potential of graph structures to address LLM limitations, and discuss applications and future directions of Graph ML. Despite the challenges in operational efficiency, techniques like parameter fine-tuning and model pruning show promise in overcoming these obstacles. LLMs offer solutions for semantic extraction and feature alignment in Graph ML, with applications in various fields like robot task planning and AI for science. The study provides insights into the evolution of graph learning methods, the integration of LLMs, and the ongoing progress in the field, highlighting the need for further exploration to advance Graph ML capabilities.

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Source link: https://www.marktechpost.com/2024/04/26/integrating-large-language-models-with-graph-machine-learning-a-comprehensive-review/?amp