arXiv:2504.19667v1 [cs.LG] 28 Apr 2025 Large Language Models (LLMs) have shown remarkable capabilities across various domains, yet they struggle with knowledgeintensive tasks in areas that demand factual accuracy, such as in industrial automation and healthcare. Key limitations include their tendency to hallucinate, lack of source traceability (provenance), and challenges in timely knowledge updates. Retrieval Augmented Generation (RAG) techniques have attempted to address these issues by incorporating external knowledge, but they face their own limitations,.... Combining language models with knowledge graphs (GraphRAG) offers promising avenues for overcoming these deficits. However, a major challenge lies in creating such a knowledge graph in the first place. Construir KG usando LLM só empurra o problema para o KG While language models (LLMs) have demonstrated impressive capabilities, they still have their limitations in knowledge-intensive tasks - especially i...