Searching for Best Practices in Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2407.01219v1
• Date: Mon, 1 Jul 2024 12:06:34 GMT
• Title: Searching for Best Practices in Retrieval-Augmented Generation
• Authors: Xiaohua Wang, Zhenghua Wang, Xuan Gao, Feiran Zhang, Yixin Wu, Zhibo Xu, Tianyuan Shi, Zhengyuan Wang, Shizheng Li, Qi Qian, Ruicheng Yin, Changze Lv, Xiaoqing Zheng, Xuanjing Huang,
• Abstract summary: Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information. Here, we investigate existing RAG approaches and their potential combinations to identify optimal RAG practices. We suggest several strategies for deploying RAG that balance both performance and efficiency.
• Score: 31.438681543849224
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information, mitigating hallucinations, and enhancing response quality, particularly in specialized domains. While many RAG approaches have been proposed to enhance large language models through query-dependent retrievals, these approaches still suffer from their complex implementation and prolonged response times. Typically, a RAG workflow involves multiple processing steps, each of which can be executed in various ways. Here, we investigate existing RAG approaches and their potential combinations to identify optimal RAG practices. Through extensive experiments, we suggest several strategies for deploying RAG that balance both performance and efficiency. Moreover, we demonstrate that multimodal retrieval techniques can significantly enhance question-answering capabilities about visual inputs and accelerate the generation of multimodal content using a "retrieval as generation" strategy.

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