Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?

arXiv:2309.16595v3 Announce Type: replace
Abstract: Large language models (LLMs) are gaining increasing attention for their capability to process graphs with rich text attributes, especially in a zero-shot fashion. Recent studies demonstrate that LLMs obtain decent text classification performance on common text-rich graph benchmarks, and the performance can be improved by appending encoded structural information as natural languages into prompts. We aim to understand why the incorporation of structural information inherent in graph data can improve the prediction performance of LLMs. …

abstract arxiv attention benchmarks capability classification cs.ai cs.lg fashion graph graphs information language language models large language large language models llms performance process prompts studies text text classification through type zero-shot