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Supervised Fine-Tuning of Large Language Models for Domain-Specific Knowledge Graph Construction: A Case Study on Hunan's Historical Celebrities
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This method proposes a supervised fine-tuning approach for Large Language Models to enhance information extraction for domain-specific Knowledge Graph construction. It involves defining a fine-grained schema, constructing an instruction fine-tuning dataset, and employing LoRA for parameter-efficient fine-tuning of LLMs. The fine-tuned LLMs are then used to extract structured entities, relations, and properties from text to build a Knowledge Graph.
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