Parameter-Efficient Tuning Large Language Models for Graph Representation Learning

arXiv:2404.18271v1 Announce Type: cross
Abstract: Text-rich graphs, which exhibit rich textual information on nodes and edges, are prevalent across a wide range of real-world business applications. Large Language Models (LLMs) have demonstrated remarkable abilities in understanding text, which also introduced the potential for more expressive modeling in text-rich graphs. Despite these capabilities, efficiently applying LLMs to representation learning on graphs presents significant challenges. Recently, parameter-efficient fine-tuning methods for LLMs have enabled efficient new task generalization with minimal time and memory …

abstract applications arxiv business business applications cs.cl cs.lg graph graph representation graphs information language language models large language large language models llms modeling nodes representation representation learning text textual type understanding world