A Research of Challenges and Solutions in Retrieval Augmented Generation (RAG) Systems
This paper evaluates various advanced solutions proposed in recent literature, comparing their efficacy and discussing the trade-offs involved, and delves into the central architecture of RAG systems, encompassing retrieval components, generative components, and knowledge bases.
Abstract
Retrieval-Augmented Generation (RAG) systems represent a significant innovation in the field of Natural Language Processing (NLP), ingeniously integrating Large Language Models (LLMs) with dynamic external knowledge retrieval. This amalgamation not only enhances the models' responsiveness to real-world knowledge but also addresses the limitations of conventional generative models in terms of knowledge update velocity and factual accuracy. This review examines the challenges faced by RAG systems and their solutions. It delves into the central architecture of RAG systems, encompassing retrieval components, generative components, and knowledge bases, with a particular focus on recent advancements that have expanded the boundaries of performance and functionality. The study critically analyzes major challenges such as retrieval efficiency and dynamic knowledge management. This paper evaluates various advanced solutions proposed in recent literature, comparing their efficacy and discussing the trade-offs involved. Ultimately, this paper aims to provide researchers, developers, and users of RAG systems with a comprehensive perspective, fostering ongoing innovation and the expansion of applications in this domain.