Korean Paper Based Retrieval Augmented Generation Dataset 


Vol. 53,  No. 3, pp. 205-216, Mar.  2026
10.5626/JOK.2026.53.3.205


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  Abstract

Large language models (LLMs) trained on general domain data have limitations in specialized fields that are rich in information and technical terminology. Retrieval-augmented generation (RAG) improves answer accuracy and reliability by referencing external knowledge, making it particularly effective in specialized domains where pre-training data is scarce. However, there is a lack of public datasets for Korean specialized domains, highlighting the need for a dedicated retrieval-augmented generation dataset. This paper introduces a new Korean RAG dataset based on scientific and technical papers to support research in this area. We preprocessed existing document-query data to create a searchable corpus and extracted key phrases and key sentences suited for specialized applications. Additionally, we conducted a comprehensive quantitative evaluation of the dataset‘s quality. By reflecting the unique characteristics of scientific and technical papers, this dataset serves as a robust foundation for Korean RAG systems.


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  Cite this article

[IEEE Style]

J. Han, M. Choi, K. Kim, Y. Ko, "Korean Paper Based Retrieval Augmented Generation Dataset," Journal of KIISE, JOK, vol. 53, no. 3, pp. 205-216, 2026. DOI: 10.5626/JOK.2026.53.3.205.


[ACM Style]

Junho Han, Minjun Choi, Keunha Kim, and Youngjoong Ko. 2026. Korean Paper Based Retrieval Augmented Generation Dataset. Journal of KIISE, JOK, 53, 3, (2026), 205-216. DOI: 10.5626/JOK.2026.53.3.205.


[KCI Style]

한준호, 최민준, 김근하, 고영중, "한국어 논문 기반 검색 증강 생성 데이터셋," 한국정보과학회 논문지, 제53권, 제3호, 205~216쪽, 2026. DOI: 10.5626/JOK.2026.53.3.205.


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