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Sentence-Based Extraction Methodology from External References to Enhance Performance in RAG

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(12), pp.29-39
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : November 6, 2024
  • Accepted : December 4, 2024
  • Published : December 31, 2024

Myoungkuk Nam 1 Namgyu Kim 1

1국민대학교

Accredited

ABSTRACT

The reliability of Large Language Models(LLMs) can be compromised by limitations in up-to-date information and gaps in specific domain knowledge, often leading to issues like hallucination and decreased trustworthiness. To address these challenges, Retrieval-Augmented Generation(RAG) models are increasingly utilized, allowing LLMs to provide relevant answers by leveraging external data without additional training. While much research has demonstrated the potential of RAG models to enhance the reliability of LLMs, there has been limited investigation into how best to utilize external resources to improve RAG model performance. In this study, we propose a methodology to enhance RAG model performance through sentence-based extraction of external reference materials. To evaluate our proposed methodology, we conducted a Q&A task in a specialized domain (Military English) using 5,006 abbreviations and acronyms. We compared the accuracy of an LLM and two types of RAG models (simple text extraction and sentence-based extraction), finding that our proposed approach outperformed the other models.

Citation status

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