arXiv Machine Learning
Teaching LLMs to See Graphs: Unifying Text and Structural Reasoning
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Sep 4
LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Models
The paper introduces PromptGFM, a Graph Foundation Model designed for text‑attributed graphs (TAGs). It integrates Large Language Models (LLMs) and Graph Neural Networks (GNNs) through a Graph Understanding Module that prompts LLMs to emulate GNN workflows, and a Graph Inference Module that creates a language‑based graph vocabulary for better alignment and scalability. Experiments show PromptGFM outperforms existing methods and transfers effectively across various graphs and tasks.
arXiv AI
Jul 21
GRIP: In-Parameter Graph Reasoning through Fine-Tuning Large Language Models
arXiv:2511. 07457v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in modeling sequential textual data and generalizing across diverse tasks.
arXiv Machine Learning
Jun 18
Formalizing and Mitigating Structural Distortion in LLM Attention for Graph Reasoning
arXiv:2606. 15633v2 Announce Type: replace Abstract: Large Language Models (LLMs) have shown promise for reasoning over Text-Attributed Graphs (TAGs).
arXiv AI
Jun 11
GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning
arXiv:2510. 04567v3 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) are powerful tools for processing relational data but often struggle to generalize to unseen graphs, giving rise to the development of Graph Foundational Models (GFMs).
arXiv Machine Learning
Jul 1
TAG-DLM: Diffusion Language Models for Text-Attributed Graph Learning
arXiv:2606. 31166v1 Announce Type: cross Abstract: Text-attributed graphs (TAGs), where each node carries a natural language description, require models to jointly reason over text and graph topology.
arXiv Machine Learning
Jun 16
Formalizing and Mitigating Structural Distortion in LLM Attention for Zero-Shot Graph Reasoning
arXiv:2606. 15633v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown promise for reasoning over Text-Attributed Graphs (TAGs).