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NLP-AKG: Few-Shot Construction of NLP Academic Knowledge Graph Based on LLM
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This method focuses on constructing a novel knowledge graph (NLP-AKG) for NLP academic papers. It leverages LLMs for various tasks like entity extraction from text and tables, interpreting table hierarchies, generating code for data extraction, and summarizing innovation points. While also involving XLNet and k-means for cleaning and disambiguation, the core framework uses LLMs to address entity discovery, coreference resolution, and relation extraction, making it an LLM-augmented approach for KG construction.
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NLP-AKG Construction Method based on LLM
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This method aims to enhance the question answering ability of LLMs by augmenting them with knowledge from the constructed NLP-AKG during the inference stage. It involves using LLMs for intent identification, querying the KG to form 'sub-graph communities' around relevant papers, and then feeding these structured community elements along with prompts to the LLM to generate more accurate answers. This directly improves LLM performance by providing external, structured knowledge at inference.
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