Retrieval-AugmentedGenerationforLargeLanguageModels:ASur

标题：Retrieval-Augmented Generation for Large Language Models: A Survey

作者：Yunfan Gaoa , Yun Xiongb , Xinyu Gaob , Kangxiang Jiab , Jinliu Panb , Yuxi Bic , Yi Daia , Jiawei Suna , Meng Wangc , and Haofen Wang

1. By referencing external knowledge, RAG effectively reduces the problem of generating factually incorrect content. Its integration into LLMs has resulted in widespread adoption, establishing RAG as a key technology in advancing chatbots and enhancing the suitability of LLMs for real-world applications

2. The RAG research paradigm is continuously evolving, and we categorize it into three stages: Naive RAG, Advanced RAG, and Modular RAG

3. The Naive RAG：

Indexing starts with the cleaning and extraction of raw data

Retrieval. Upon receipt of a user query, the RAG system employs the same encoding model utilized during the indexing phase to transform the query into a vector representation.

Generation. The posed query and selected documents are synthesized into a coherent prompt to which a large language model is tasked with formulating a response.

4. 

Advanced RAG introduces specific improvements to overcome the limitations of Naive RAG. Focusing on enhancing retrieval quality, it employs pre-retrieval and post-retrieval strategies.

5. 

Pre-retrieval process. In this stage, the primary focus is on optimizing the indexing structure and the original query. The goal of optimizing indexing is to enhance the quality of the content being indexed.

Post-Retrieval Process. Once relevant context is retrieved, it’s crucial to integrate it effectively with the query

6. Innovations such as the Rewrite-Retrieve-Read [7]model leverage the LLM’s capabilities to refine retrieval queries through a rewriting module and a LM-feedback mechanism to update rewriting model

7. RAG is often compared with Fine-tuning (FT) and prompt engineering. Each method has distinct characteristics as illustrated in Figure 4.

8. In the context of RAG, it is crucial to efficiently retrieve relevant documents from the data source. There are several key issues involved, such as the retrieval source, retrieval granularity, pre-processing of the retrieval, and selection of the corresponding embedding model.