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