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CogMG is a collaborative augmentation framework where an LLM, acting as an agent, decomposes queries, identifies knowledge gaps in KGs, completes missing facts using its internal parameters, and facilitates the evolution of the KG through verified additions. This mutual enhancement directly improves LLM's factual accuracy in QA by leveraging the updated KG, while also enriching the KG with LLM-generated and verified knowledge, thereby enhancing reasoning across both components.
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