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.

翻译：数据标注是构建新数据集的关键步骤。然而，传统通过众包进行数据标注的方式既耗时又昂贵。此外，由于众包工作者语言背景的差异，处理低资源语言时这一流程的复杂性会进一步增加。为解决这些问题，本研究提出了一种利用大语言模型的自主标注方法——近期研究表明此类模型展现出卓越性能。通过实验，我们论证了该方法不仅具有成本效益，还可适用于低资源语言的标注工作。同时，我们采用该方法构建了图像描述数据集，并承诺将该数据集开放以供后续研究。为促进进一步研究及确保结果可复现，我们已公开了相关源代码。