Dynamic Bundling with Large Language Models for Zero-Shot Inference on Text-Attributed Graphs

Understand

Large language models (LLMs) have been used in many zero-shot learning problems, with their strong generalization ability.

• Recently, adopting LLMs in text-attributed graphs (TAGs) has drawn increasing attention.
• However, the adoption of LLMs faces two major challenges: limited information on graph structure and unreliable responses.
• LLMs struggle with text attributes isolated from the graph topology.