Search Results for author:
Found 5 papers, 0 papers with code
TAGA: Text-Attributed Graph Self-Supervised Learning by Synergizing Graph and Text Mutual Transformations
Text-Attributed Graphs (TAGs) enhance graph structures with natural language descriptions, enabling detailed representation of data and their relationships across a broad spectrum of real-world scenarios.
GRAG: Graph Retrieval-Augmented Generation
To address this challenge, we introduce $\textbf{Graph Retrieval-Augmented Generation (GRAG)}$, which significantly enhances both the retrieval and generation processes by emphasizing the importance of subgraph structures.
ELAD: Explanation-Guided Large Language Models Active Distillation
The deployment and application of Large Language Models (LLMs) is hindered by their memory inefficiency, computational demands, and the high costs of API inferences.
Distilling Large Language Models for Text-Attributed Graph Learning
To address the inherent gaps between LLMs (generative models for texts) and graph models (discriminative models for graphs), we propose first to let LLMs teach an interpreter with rich textual rationale and then let a student model mimic the interpreter's reasoning without LLMs' textual rationale.
Beyond Text: A Deep Dive into Large Language Models' Ability on Understanding Graph Data
Large language models (LLMs) have achieved impressive performance on many natural language processing tasks.
