  Data annotation is an essential step for constructing new datasets. However,
the conventional approach of data annotation through crowdsourcing is both
time-consuming and expensive. In addition, the complexity of this process
increases when dealing with low-resource languages owing to the difference in
the language pool of crowdworkers. To address these issues, this study proposes
an autonomous annotation method by utilizing large language models, which have
been recently demonstrated to exhibit remarkable performance. Through our
experiments, we demonstrate that the proposed method is not just cost-efficient
but also applicable for low-resource language annotation. Additionally, we
constructed an image captioning dataset using our approach and are committed to
open this dataset for future study. We have opened our source code for further
study and reproducibility.
