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GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning
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GNN-RAG enhances LLM performance on KGQA by using a GNN to retrieve relevant multi-hop reasoning paths from a KG. These verbalized paths are then fed to the LLM as context for RAG, thereby improving the LLM's ability to answer complex questions during its inference stage and reducing hallucinations.
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GNN-RAG+RA further boosts LLM KGQA performance by employing retrieval augmentation, combining GNN-induced reasoning paths with LLM-based retrieved paths (e.g., from RoG). This method increases the diversity and recall of retrieved KG information, which in turn enhances the LLM's reasoning and answer accuracy during inference.
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