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It involves fine-tuning an LLM for knowledge triples extraction from unsupervised corpora, followed by post-processing for error removal and entity resolution. This directly addresses the problem of knowledge mismatch by building a tailored KG. https://neverblink.eu/ontologies/llm-kg/methods#EfficientLlmKgConstruction http://www.w3.org/2000/01/rdf-schema#label Efficient construction of domain KGs https://neverblink.eu/ontologies/llm-kg/methods#EfficientLlmKgConstruction https://neverblink.eu/ontologies/llm-kg/hasTopCategory https://neverblink.eu/ontologies/llm-kg/top-categories#LLMAugmentedKG https://neverblink.eu/ontologies/llm-kg/methods#EnhancedLlmWithKnowledgePrelearningAndFeedback http://purl.org/dc/terms/subject https://neverblink.eu/ontologies/llm-kg/categories#SynergizedReasoning https://neverblink.eu/ontologies/llm-kg/methods#EnhancedLlmWithKnowledgePrelearningAndFeedback http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/spar/fabio/Workflow https://neverblink.eu/ontologies/llm-kg/methods#EnhancedLlmWithKnowledgePrelearningAndFeedback http://www.w3.org/2000/01/rdf-schema#comment ELPF is a modular, three-stage KG-LLM alignment framework designed to enhance LLM's capability to utilize KG information and reduce hallucinations. It includes K-LoRA for pre-learning KG infusion and domain linguistic style, supervised fine-tuning with KG retrieval, and Alignment with Knowledge Graph Feedback (AKGF) where KGs act as automated evaluators for DPO-based fine-tuning. 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