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Optimizing Query Generation for Enhanced Document Retrieval in RAG
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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%.
Forward citations
Cited by 2 Pith papers
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Hierarchical Reranking for Scalable Financial RAG System
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.
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Multimodal Hybrid Retrieval-Augmented Generation for Scientific Document Understanding using Open-Source SLMs
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.
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