[!PDF|yellow] Page.1

they still encounter high computational costs due to iterative retrieval and reasoning with LLMs

[!PDF|yellow] Page.2

However, these algorithms rely solely on the graph structure, which is often noisy or incomplete, limiting their overall performance

[!PDF|yellow] Page.2

Nevertheless, they still face limitations in generalizability since they require training from scratch on new datasets.

[!PDF|yellow] Page.2

However, these methods primarily focus on graph-related tasks (e.g., node classification and link prediction), leaving the design of a GFM to enhance LLMs’ reasoning ability unexplored.

只考虑了图相关任务而没有考虑 RAG 的能力