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Retrieval-Augmented Generation for Large Language Models: A Survey

arxiv.org · 16,524 words · saved by 1 readers

This is experimental HTML to improve accessibility. We invite you to report rendering errors. Use Alt+Y to toggle on accessible reporting links and Alt+Shift+Y to toggle off. Learn more about this project and help improve conversions. Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes.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

Retrieval-Augmented Generation for Large Language Models: A Survey Yunfan Gao Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University Yun Xiong Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University Xinyu Gao Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University Kangxiang Jia Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University Jinliu Pan Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University Yuxi Bi College of Design and Innovation, Tongji Un

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