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Cross-Data Knowledge Graph Construction for LLM-enabled Educational Question-Answering System: A Case Study at HCMUT

A case study on constructing a knowledge graph to enhance the performance of large language models in educational question-answering systems.

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Cross-Data Knowledge Graph Construction for LLM-enabled Educational Question-Answering System: A Case Study at HCMUT

By Tuan Van Bui, Oanh Tran, Phuong Hanh Nguyen, B. Y. K. HO, Long Nguyen, Thang Bui, Tho Quan
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This paper presents a case study on constructing a cross-data knowledge graph to improve the performance of large language models (LLMs) in educational question-answering systems.

The authors propose integrating LLMs with knowledge graphs to provide factual context and address limitations such as remembering events and incorporating new information.

Abstract

This paper presents a case study on constructing a cross-data knowledge graph to improve the performance of large language models (LLMs) in educational question-answering systems. The authors propose integrating LLMs with knowledge graphs to provide factual context and address limitations such as remembering events and incorporating new information.

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knowledge graph constructionllm integrationeducational question answeringfactual contextartificial intelligenceKnowledge GraphsLarge Language ModelsRetrieval & RAGSemantic Interoperability
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