mlsanityData to 14 Oct 2026

Optimizing Query Generation for Enhanced Document Retrieval in RAG

arXiv 2407.12325 ↗First posted 17 Jul 2024cs.IRcited by 10

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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%.

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

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 5; 2026: 3.

10 papers.

  1. 2024Multi-Reranker: Maximizing performance of retrieval-augmented generation in the FinanceRAG challengecited by 10
  2. 2025Optimizing Retrieval Strategies for Financial Question Answering Documents in Retrieval-Augmented Generation Systemscited by 9
  3. 2025LegalRAG: A Hybrid RAG System for Multilingual Legal Information Retrievalcited by 5
  4. 2024Self-adaptive Multimodal Retrieval-Augmented Generationcited by 4
  5. 2025OpenRAG: Optimizing RAG End-to-End via In-Context Retrieval Learningcited by 4
  6. 2025Align to Misalign: Automatic LLM Jailbreak with Meta-Optimized LLM Judgescited by 2
  7. 2025A GenAI System for Improved FAIR Independent Biological Database Integrationcited by 1
  8. 2026RegGuard: AI-Powered Retrieval-Enhanced Assistant for Pharmaceutical Regulatory Compliancecited by 0
  9. 2026RAG without Forgetting: Continual Query-Infused Key Memorycited by 0
  10. 2026Multimodal Hybrid Retrieval-Augmented Generation for Scientific Document Understanding using Open-Source SLMscited by 0

Similar, not cited

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  1. 2026Progressive Searching for Retrieval in RAG
  2. 2024Enhancing Retrieval Processes for Language Generation with Augmented Queries
  3. 2024Meta Knowledge for Retrieval Augmented Large Language Models
  4. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey
  5. 2024A Survey on Retrieval-Augmented Text Generation for Large Language Models
  6. 2024Developing Retrieval Augmented Generation (RAG) based LLM Systems from PDFs: An Experience Report
  7. 2025Less LLM, More Documents: Searching for Improved RAG
  8. 2024Improving Retrieval for RAG based Question Answering Models on Financial Documents