Labeling corpora constitutes a bottleneck to create models for new tasks or
domains. Large language models mitigate the issue with automatic corpus
labeling methods, particularly for categorical annotations. Some NLP tasks such
as emotion intensity prediction, however, require text regression, but there is
no work on automating annotations for continuous label assignments. Regression
is considered more challenging than classification: The fact that humans
perform worse when tasked to choose values from a rating scale lead to
comparative annotation methods, including best-worst scaling. This raises the
question if large language model-based annotation methods show similar
patterns, namely that they perform worse on rating scale annotation tasks than
on comparative annotation tasks. To study this, we automate emotion intensity
predictions and compare direct rating scale predictions, pairwise comparisons
and best-worst scaling. We find that the latter shows the highest reliability.
A transformer regressor fine-tuned on these data performs nearly on par with a
model trained on the original manual annotations.

大语言模型对于分类注释的自动化主要用于处理标注语料库的瓶颈问题，但对于连续标签分配的自动化注释尚无相关研究。本研究通过对情感强度进行自动化预测，并比较直接使用评分标注、两两比较和最佳 - 最差比例三种方法，发现最后一种方法具有最高的可靠性。在这些数据上微调的 Transformer 回归器表现几乎与原始手动标注模型相当。