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RAG for LLMs: An Overview
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This paper examines the rapidly developing field of Retrieval-Augmented Generation (RAG), which aims to improve the capabilities of Large Language Models (LLMs) by incorporating external knowledge. The paper reviews the evolution of RAG paradigms, from the early "Naive RAG" to the more sophisticated "Advanced RAG" and "Modular RAG" approaches. It explores the three core components of RAG systems—retrieval, generation, and augmentation—examining the techniques used in each and highlighting the state-of-the-art technologies within these areas. The paper also provides an updated evaluation framework and benchmark for RAG, outlines the challenges faced by the technology, and presents potential avenues for future research and development.
Read it here: https://arxiv.org/abs/2312.10997v5
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