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  • A Systematic Literature Review of Retrieval-Augmented . . .
    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 A total of 128 articles met our inclusion criteria The records were retrieved from ACM Digital Library, IEEE Xplore, Scopus, ScienceDirect, and the Digital Bibliography and Library Project (DBLP) RAG
  • 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 Systematic Literature Review of Retrieval-Augmented . . .
    Finally, Section 7 concludes by summarising the main findings and offering recommendations for the design, evaluation, and deployment of retrieval-augmented generation systems These sections address the guiding research questions by providing a transparent, PRISMA-aligned synthesis of the current state of knowledge on RAG
  • (PDF) 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
  • Systematic Review: Retrieval-Augmented Generation
    Systematic Literature Review of Retrieval-Augmented Generation: Techniques, Metrics, and Challenges Introduction This systematic review synthesizes the post-2020 research landscape on Retrieval-Augmented Generation (RAG), focusing on highly cited work selected by a PRISMA-compliant protocol RAG architectures interleave neural information retrieval with generative LMs, aiming to ground model
  • 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
  • Retrieval augmented generation for 10 large language models . . .
    The LLM-RAG models took on average 1 s for retrieval and 15–20 s for results generation, while the human evaluators took an average of 10 min to generate the full preoperative instructions





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