KIT-19: A Comprehensive Korean Instruction Toolkit on 19 Tasks for
  Fine-Tuning Korean Large Language Models
Dongjun Jang Sungjoo Byun Hyemi Jo Hyopil Shin
Instruction Tuning on Large Language Models is an essential process for model
to function well and achieve high performance in specific tasks. Accordingly,
in mainstream languages such as English, instruction-based datasets are being
constructed and made publicly available. In the case of Korean, publicly
available models and datasets all rely on using the output of ChatGPT or
translating datasets built in English. In this paper, We introduce
\textit{KIT-19} as an instruction dataset for the development of LLM in Korean.
\textit{KIT-19} is a dataset created in an instruction format, comprising 19
existing open-source datasets for Korean NLP tasks. In this paper, we train a
Korean Pretrained LLM using \textit{KIT-19} to demonstrate its effectiveness.
The experimental results show that the model trained on \textit{KIT-19}
significantly outperforms existing Korean LLMs. Based on the its quality and
empirical results, this paper proposes that \textit{KIT-19} has the potential
to make a substantial contribution to the future improvement of Korean LLMs'
performance.