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Optimizing Query Generation for Enhanced Document Retrieval in RAG

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arxiv 2407.12325 v1 pith:X6D3NXHR submitted 2024-07-17 cs.IR

Hamin Koo , Minseon Kim , Sung Ju Hwang This is my paper · ORCID
classification cs.IR
keywords documentretrievalgenerationaimshallucinationslanguagellmsoptimizing
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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%.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A GenAI System for Improved FAIR Independent Biological Database Integration

    cs.IR 2025-06 conditional novelty 5.0 of 10

    FAIRBridge is a GenAI system that discovers biological databases from literature and uses GPT-4o plus web wrappers to retrieve and integrate their data through natural language queries.

  2. Hierarchical Reranking for Scalable Financial RAG System

    cs.IR 2026-07 reject novelty 4.0 of 10

    A finance-specific RAG pipeline combining table-to-JSON conversion, two-stage reranking, and long-context split-fusion reports NDCG@20=0.7918 and second place in the ICAIF '24 FinanceRAG challenge.

  3. Multimodal Hybrid Retrieval-Augmented Generation for Scientific Document Understanding using Open-Source SLMs

    cs.IR 2026-07 conditional novelty 4.0 of 10

    A fully local open-source RAG system with VLM-based multimodal ingestion and hybrid retrieval matched cloud summarization quality and improved retrieval MRR from 0.132 to 0.349 on a synthetic benchmark.

  4. LegalRAG: A Hybrid RAG System for Multilingual Legal Information Retrieval

    cs.IR 2025-04 conditional novelty 4.0 of 10

    An advanced RAG pipeline with an extra relevance-check and query-refinement model improves Bangla legal document QA over a vanilla RAG baseline on a 168-pair test set.

  5. Agentic AI-Driven Technical Troubleshooting for Enterprise Systems: A Novel Weighted Retrieval-Augmented Generation Paradigm

    cs.AI 2024-12 conditional novelty 4.0 of 10

    The paper claims a dynamically weighted RAG framework improves enterprise troubleshooting accuracy to 90.8% versus 85.2% for standard RAG, though supporting details are sparse.

  6. Multi-Reranker: Maximizing performance of retrieval-augmented generation in the FinanceRAG challenge

    cs.CL 2024-11 conditional novelty 2.0 of 10

    By adding keyword-based query expansion, dataset-specific reranker selection, and a 32k token context split, the system reached second place in the FinanceRAG 2024 competition.

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