GNN-RAG: combining LLMs language abilities with GNNs reasoning in RAG style
Knowledge Graphs (KGs) represent human-crafted factual knowledge in the form of triplets (head, relation, tail), which collectively form a…
GraCoRe: Benchmarking Graph Comprehension and Complex Reasoning in Large Language Models
Can LLMs understand graphs? The results might surprise you. Graphs are everywhere, from social networks to biological pathways. As AI systems become more…
GraCoRe: Benchmarking Graph Comprehension and Complex Reasoning in Large Language Models
GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning
Knowledge Graphs (KGs) represent human-crafted factual knowledge in the form of triplets (head, relation, tail), which collectively form a graph. Question Answering over KGs (KGQA) is the task of...
Semantic search and its supplement ‘Graph based prompting’ | by Jeong ii tae | Mar, 2024 | Medium
Graph Neural Prompting with Large Language Models
Graph ML meets Language Models
Where does current work on graph technologies intersect with large language models, for building AI apps?