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A Systematic Literature Review of Retrieval-Augmented . . .
Abstract—This systematic review of the research literature on retrieval-augmented generation (RAG) provides a focused analysis of the most highly cited studies published between 2020 and May 2025
Retrieval-Augmented Generation for AI-Generated Content: A . . .
Retrieval-augmented generation technology in mathematics streamlines problem-solving, boosts research innovation, and refines educational strategies LeanDojo [343] boosts theorem proving by using retrieval-augmented methods to choose relevant premises from extensive mathematical libraries, improving automation and theorem generalization
A Comprehensive Survey of Retrieval-Augmented Generation (RAG . . .
Abstract This paper presents a comprehensive study of Retrieval-Augmented Generation (RAG), tracing its evolution from foundational concepts to the current state of the art
Retrieval Augmented Generation Evaluation in the Era of Large . . .
In addi-tion, current research indicates defense mechanisms remain insufi cient against sophisticated attacks [86–88] Evalua-tions reveal significant vulnerabilities in current RAG sys-tems [87, 88], underscoring the need for robust benchmarks and metrics addressing the unique safety challenges arising from the retrieval-generation interplay
Enhancing Retrieval-Augmented Generation: A Study of Best . . .
Abstract Retrieval-Augmented Generation (RAG) systems have recently shown remarkable advancements by integrating retrieval mechanisms into language models, enhancing their ability to produce more accurate and contextually relevant responses However, the influence of various components and configurations within RAG systems remains underexplored
Research on the construction and application of retrieval . . .
This study proposes an innovative Knowledge Graph Augmented Retrieval and Generation (KG-RAG) model, which combines structured knowledge graphs with generative models to optimize knowledge
A Comprehensive Survey of Retrieval-Augmented Generation (RAG . . .
This paper presents a comprehensive study of Retrieval-Augmented Generation (RAG), tracing its evolution from foundational concepts to the current state of the art RAG combines retrieval mechanisms with generative language models to enhance the accuracy of outputs, addressing key limitations of LLMs