Enhancing Retrieval-Augmented Generation: Efficient Quote Extraction for Scalable and Accurate NLP Systems

LLMs have significantly advanced natural language processing, excelling in tasks like open-domain question answering, summarization, and conversational AI. However, their growing size and computational demands highlight inefficiencies in managing extensive contexts, particularly in functions requiring complex reasoning and retrieving specific information. To address this, Retrieval-Augmented Generation (RAG) combines retrieval systems with generative models, allowing access […]

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