2024-07-01
arXiv

Searching for Best Practices in Retrieval-Augmented Generation

Zhenghua Wang , Xuanjing Huang , Xiaoqing Zheng , Changze Lv , Ruicheng Yin
The paper investigates different RAG approaches to identify optimal practices that balance performance and efficiency, and shows that multimodal retrieval techniques can enhance question-answering and content generation.
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.
2023-12-18
arXiv

Retrieval-Augmented Generation for Large Language Models: A Survey

Haofen Wang , Meng Wang , Jiawei Sun , Yi Dai , Yuxi Bi
The paper reviews Retrieval-Augmented Generation (RAG) for Large Language Models, which integrates external knowledge to improve accuracy and credibility. It covers the evolution of RAG paradigms and their components, and introduces a new evaluation framework. The paper also discusses current challenges and future research directions.
Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes. Retrieval-Augmented Generation (RAG) has emerged as a promising solution by incorporating knowledge from external databases. This enhances the accuracy and credibility of the generation, particularly for knowledge-intensive tasks, and allows for continuous knowledge updates and integration of domain-specific information. RAG synergistically merges LLMs' intrinsic knowledge with the vast, dynamic repositories of external databases. This comprehensive review paper offers a detailed examination of the progression of RAG paradigms, encompassing the Naive RAG, the Advanced RAG, and the Modular RAG. It meticulously scrutinizes the tripartite foundation of RAG frameworks, which includes the retrieval, the generation and the augmentation techniques. The paper highlights the state-of-the-art technologies embedded in each of these critical components, providing a profound understanding of the advancements in RAG systems. Furthermore, this paper introduces up-to-date evaluation framework and benchmark. At the end, this article delineates the challenges currently faced and points out prospective avenues for research and development.