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 excel in various language tasks but they often generate incorrect information%2C a phenomenon known as hallucinations. Retrieval-Augmented Generation (RAG) aims to mitigate this by using document retrieval for accurate responses. However%2C RAG still faces hallucinations due to vague queries. This study aims to improve RAG by optimizing query generation with a query-document alignment score%2C refining queries using LLMs for better precision and efficiency of document retrieval. Experiments have shown that our approach improves document retrieval%2C resulting in an average accuracy gain of 1.6%25.?quality=80&w=800)
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