A Retrieval Augmented Generation(RAG) System Using Query Rewritting Based on Large Langauge Model(LLM)

Minsu Han, Seokyoung Hong, Myoung-Wan Koo

http://doi.org/10.5626/JOK.2025.52.6.474

This paper proposes a retrieval pipeline that can be effectively utilized in fields requiring expert knowledge without requiring fine-tuning. To achieve high accuracy, we introduce a query rewriting retrieval method that leverages large language models to generate examples similar to the given question, achieving higher similarity than existing retrieval models. The proposed method demonstrates excellent performance in both automated evaluations and expert qualitative assessments, while also providing explainability in retrieval results through generated examples. Additionally, we suggest prompts that can be utilized in various domains requiring specialized knowledge during the application of this method. Furthermore, we propose a pipeline method that incorporates a Top-1 retrieval model, which chooses the most relevant document from the three returned by the query rewriting retrieval model. This aims to prevent the hallucination issue caused by the input of unnecessary documents into the large language model.