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The motivation behind this topic is to explore the potential of Retrieval-Augmented Generation (RAG) in enhancing the performance of natural language processing tasks, particularly in the context of question answering systems. By leveraging external knowledge sources, RAG combines the strengths of information retrieval and language generation, improving the accuracy and relevance of generated responses. The goal of this study is to evaluate how RAG can be applied to solve complex, knowledge-intensive tasks and to assess its effectiveness in real-world scenarios, particularly in applications such as intelligent virtual assistants and automated customer support systems.
why i write and cann't answer my question?
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