  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.
