Chuang Zhou

Graph-based Retrieval-Augmented Generation (GraphRAG) enhances the reasoning capabilities of Large Language Models (LLMs) by grounding their responses in structured knowledge graphs. Leveraging community detection and relation filtering techniques, GraphRAG systems demonstrate inherent resistance...

Retrieval-Augmented Generation (RAG) has demonstrated significant potential in enhancing large language models (LLMs) by supplementing external knowledge. However, existing approaches focus primarily on retrieving isolated factual knowledge entities while neglecting the critical reasoning relatio...

Retrieval-Augmented Generation (RAG) has long been a promising paradigm for enhancing large language models (LLMs) with external knowledge. Traditional embedding-based methods for graph construction can capture semantic similarity but struggle to establish fine-grained, interpretable logical rela...