A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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This paper proposes a graph-based RAG framework that enhances document retrieval by integrating knowledge graphs with LLMs to improve answer accuracy and context relevance.
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GraphRAG enhances LLM-driven RAG for finance by using graph-based retrieval to reduce hallucinations and improve contextual coherence.
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This paper proposes an economical communication pipeline for LLM-based multi-agent systems, using a graph-based description of reasoning to reduce token costs while maintaining performance.
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GFM-RAG introduces a graph foundation model for retrieval augmented generation, enhancing knowledge integration through graph-based retrieval.
Caroline Pantofaru, Martial Hebert
This paper evaluates mean shift and graph-based segmentation algorithms using the Normalized Probabilistic Rand index for objective comparison.
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This paper proposes enhancing graph-based retrieval-augmented generation (RAG) with robust retrieval techniques to improve factual accuracy.
Junde Wu, Jiayuan Zhu, Yunli Qi
MedGraphRAG introduces a graph-based RAG framework to improve safety and accuracy of medical LLMs by structuring medical knowledge as a graph.
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MedGraphRAG enhances LLMs for evidence-based medical responses using a graph-based retrieval-augmented generation framework.
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Lightrag introduces a graph-based indexing approach to enhance efficiency in retrieval-augmented generation for LLMs.