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.

利用 KIT-19 数据集训练的韩国预训练大语言模型在性能上显著优于现有韩国大语言模型，并具备潜力从根本上改善韩国大语言模型的性能。

KIT-19：用于对韩国大型语言模型进行微调的 19 个任务的综合韩语教学工具包

KIT-19: A Comprehensive Korean Instruction Toolkit on 19 Tasks for  Fine-Tuning Korean Large Language Models

Typically, tokenization is the very first step in most text processing works.
As a token serves as an atomic unit that embeds the contextual information of
text, how to define a token plays a decisive role in the performance of a
model.Even though Byte Pair Encoding (BPE) has been considered the de facto
standard tokenization method due to its simplicity and universality, it still
remains unclear whether BPE works best across all languages and tasks. In this
paper, we test several tokenization strategies in order to answer our primary
research question, that is, "What is the best tokenization strategy for Korean
NLP tasks?" Experimental results demonstrate that a hybrid approach of
morphological segmentation followed by BPE works best in Korean to/from English
machine translation and natural language understanding tasks such as KorNLI,
KorSTS, NSMC, and PAWS-X. As an exception, for KorQuAD, the Korean extension of
SQuAD, BPE segmentation turns out to be the most effective.

测试出，对于韩国自然语言处理任务，采用基于词形的分词再使用 BPE 的混合方法效果最佳。