1 citations · 2 across the 11 of their papers we have counts for
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You Don't Need Pre-built Graphs for RAG: Retrieval Augmented Generation with Adaptive Reasoning Structures
Shengyuan Chen, Chuang Zhou, Zheng Yuan +6
Large language models (LLMs) often suffer from hallucination, generating factually incorrect statements when handling questions beyond their knowledge and perception. Retrieval-aug…
LAG: Logic-Augmented Generation from a Cartesian Perspective
Yilin Xiao, Chuang Zhou, Yujing Zhang +5
Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, yet exhibit critical limitations in knowledge-intensive tasks, often generating…
GraphRAG-Bench: Challenging Domain-Specific Reasoning for Evaluating Graph Retrieval-Augmented Generation
Yilin Xiao, Junnan Dong, Chuang Zhou +5
Graph Retrieval Augmented Generation (GraphRAG) has garnered increasing recognition for its potential to enhance large language models (LLMs) by structurally organizing domain-spec…
Reliable Reasoning Path: Distilling Effective Guidance for LLM Reasoning with Knowledge Graphs
Yilin Xiao, Chuang Zhou, Qinggang Zhang +3
Large language models (LLMs) often struggle with knowledge-intensive tasks due to a lack of background knowledge and a tendency to hallucinate. To address these limitations, integr…
Each Graph is a New Language: Graph Learning with LLMs
Huachi Zhou, Jiahe Du, Chuang Zhou +4
Recent efforts leverage Large Language Models (LLMs) for modeling text-attributed graph structures in node classification tasks. These approaches describe graph structures for LLMs…