GPTs Are Multilingual Annotators for Sequence Generation Tasks
This addresses data annotation challenges for researchers and practitioners, especially in low-resource language contexts, though it is incremental as it applies existing LLMs to a known bottleneck.
This study proposes using large language models as autonomous multilingual annotators to address the time-consuming and expensive nature of conventional crowdsourced data annotation, particularly for low-resource languages, and demonstrates the method's cost-efficiency and applicability by constructing an image captioning dataset.
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.