RUC-NLPIR/RAG-Reading-List
RAG methods, benchmarks, and toolkits
This reading list helps AI practitioners and researchers stay current with the rapidly evolving field of Retrieval-Augmented Generation (RAG). It provides a curated collection of recent academic papers and toolkits, categorized by method, benchmarks, and analysis for both text-only and multimodal applications. The list helps you understand the latest advancements, identify effective techniques, and discover resources to implement RAG in your projects.
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Use this if you are a machine learning engineer, data scientist, or AI researcher looking for a structured overview of the newest research and tools in Retrieval-Augmented Generation.
Not ideal if you are a non-technical user seeking a simple explanation of RAG or a ready-to-use RAG application without needing to delve into academic papers.
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Nov 28, 2024
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