Junho Han  Minjun Choi  Keunha Kim  Youngjoong Ko

10.5626/JOK.2026.53.3.205

Large Language Model Retrieval-Augmented Generation Information Retrieval Keyphrase Extraction response generation evaluation

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.