Knowledge-Graph Based Augmentation versus Retrieval Augmented Generation for Cultural-Related Question Answering
G-Retriever is competitive with RAG and reduces the error of the base LLM by 72\% with a standard KG and 78\% with a benchmark-aware variant, the gap to RAG narrowing further as the graph is oriented toward task-relevant content.
Pablo Poulenard, Yannis Karmim, Valentin Barrière
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