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Enhancing Large Language Models with Pseudoand Multisource-Knowledge Graphs for Open-ended Question Answering
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PG&AKV is a framework designed to enhance LLM performance during inference, specifically for open-ended and precise question answering. It leverages LLMs to generate a preliminary knowledge framework (pseudo-graph) and then uses external KGs for atomic-level knowledge querying and verification. The refined knowledge from KGs mitigates LLM hallucinations and improves answer accuracy without retraining, aligning with the goal of KGEnhancedLLMInference.
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