Large Language Models (LLMs) have emerged as powerful support tools across
various natural language tasks and a range of application domains. Recent
studies focus on exploring their capabilities for data annotation. This paper
provides a comparative overview of twelve studies investigating the potential
of LLMs in labelling data. While the models demonstrate promising cost and
time-saving benefits, there exist considerable limitations, such as
representativeness, bias, sensitivity to prompt variations and English language
preference. Leveraging insights from these studies, our empirical analysis
further examines the alignment between human and GPT-generated opinion
distributions across four subjective datasets. In contrast to the studies
examining representation, our methodology directly obtains the opinion
distribution from GPT. Our analysis thereby supports the minority of studies
that are considering diverse perspectives when evaluating data annotation tasks
and highlights the need for further research in this direction.

通过比较概述了 12 个研究探索了大型语言模型在数据标注方面的潜力，同时揭示了存在的限制，如表征性、偏见、对提示变化的敏感性和对英语的偏好。利用这些研究的见解，我们的实证分析在四个主观数据集上进一步检查了人类和生成的 GPT 意见分布之间的一致性，从而支持了少数研究在评估数据标注任务时考虑多元化观点的方法，并强调了在这个方向上进一步研究的必要性。