4 papers
See or Say Graphs: Agent-Driven Scalable Graph Structure Understanding with Vision-Language Models
Shuo Han, Yukun Cao, Zezhong Ding +3
Vision-language models (VLMs) have shown promise in graph structure understanding, but remain limited by input-token constraints, facing scalability bottlenecks and lacking effecti…
LEGO-GraphRAG: Modularizing Graph-based Retrieval-Augmented Generation for Design Space Exploration
Yukun Cao, Zengyi Gao, Zhiyang Li +3
GraphRAG integrates (knowledge) graphs with large language models (LLMs) to improve reasoning accuracy and contextual relevance. Despite its promising applications and strong relev…
FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs
Zengyi Gao, Yukun Cao, Hairu Wang +4
To mitigate the hallucination and knowledge deficiency in large language models (LLMs), Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) has shown promising potentia…
GraphInsight: Unlocking Insights in Large Language Models for Graph Structure Understanding
Yukun Cao, Shuo Han, Zengyi Gao +3
Although Large Language Models (LLMs) have demonstrated potential in processing graphs, they struggle with comprehending graphical structure information through prompts of graph de…