Kg Rag

Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating external knowledge bases to improve accuracy and reduce hallucinations, a significant challenge in current LLMs. Research focuses on improving retrieval methods, such as optimizing query rewriting and employing knowledge graphs, and enhancing the integration of retrieved information with LLM generation, including exploring novel architectures like interleaved retrieval and generation. These advancements are crucial for building more reliable and trustworthy LLMs, with applications ranging from improved customer service chatbots to more accurate question-answering systems in specialized domains.