Retrieval-Augmented Generation Paper is a breakthrough approach in natural language processing that combines retrieval-based methods with generative models. By retrieving relevant information from a knowledge base, RAG enhances the generation process, making it more contextually accurate and informative. This hybrid technique reduces reliance on memorization, improving the overall performance in tasks like question answering and summarization. RAG represents a significant step forward in improving AI's ability to generate knowledge-driven responses.
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