# Optimizing Query Generation for Enhanced Document Retrieval in RAG

arXiv: https://arxiv.org/abs/2407.12325 · HTML: https://mlsanity.com/paper/2407.12325/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2024.

## Built on by (most cited first, 10 of 10)

- [Multi-Reranker: Maximizing performance of retrieval-augmented generation in the FinanceRAG challenge](https://mlsanity.com/paper/2411.16732/) (2024)
- [Optimizing Retrieval Strategies for Financial Question Answering Documents in Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2503.15191/) (2025)
- [LegalRAG: A Hybrid RAG System for Multilingual Legal Information Retrieval](https://mlsanity.com/paper/2504.16121/) (2025)
- [Self-adaptive Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2410.11321/) (2024)
- [OpenRAG: Optimizing RAG End-to-End via In-Context Retrieval Learning](https://mlsanity.com/paper/2503.08398/) (2025)
- [Align to Misalign: Automatic LLM Jailbreak with Meta-Optimized LLM Judges](https://mlsanity.com/paper/2511.01375/) (2025)
- [A GenAI System for Improved FAIR Independent Biological Database Integration](https://mlsanity.com/paper/2506.17934/) (2025)
- [RegGuard: AI-Powered Retrieval-Enhanced Assistant for Pharmaceutical Regulatory Compliance](https://mlsanity.com/paper/2601.17826/) (2026)
- [RAG without Forgetting: Continual Query-Infused Key Memory](https://mlsanity.com/paper/2602.05152/) (2026)
- [Multimodal Hybrid Retrieval-Augmented Generation for Scientific Document Understanding using Open-Source SLMs](https://mlsanity.com/paper/2607.24799/) (2026)

## Abstract (arXiv)

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%.