Multi-Meta-RAG: Enhancing Retrieval-Augmented Generation for Complex Multi-Hop Queries
Acuvate
NOVEMBER 26, 2024
Introduction Retrieval-Augmented Generation (RAG) has emerged as a critical technique for empowering Large Language Models (LLMs) with real-time knowledge retrieval capabilities. However, traditional RAG models struggle with multi-hop queries , which require retrieving and reasoning over multiple interconnected pieces of evidence. These limitations often lead to incomplete or inaccurate answers, especially when the data spans diverse sources.
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