Large language models (LLMs) have shown remarkable promise in simulating
human language use and behavior. In this study, we delve into the intersection
of persona variables and the capability of LLMs to simulate different
perspectives. We find that persona variables can explain <10\% variance in
annotations in existing subjective NLP datasets. Nonetheless, incorporating
them via prompting in LLMs provides modest improvement. Persona prompting is
most effective on data samples where disagreements among annotators are
frequent yet confined to a limited range. A linear correlation exists: the more
persona variables influence human annotations, the better LLMs predictions are
using persona prompting. However, when the utility of persona variables is low
(i.e., explaining <10\% of human annotations), persona prompting has little
effect. Most subjective NLP datasets fall into this category, casting doubt on
simulating diverse perspectives in the current NLP landscape.

使用大型语言模型（LLMs）和个性变量在现有主观 NLP 数据集中模拟不同视角方面，发现个性变量通过提示在 LLMs 中的应用能提供适度的改善，但在解释人类标注中的作用较低（<10%）时，个性提示的效果很小，这对当前 NLP 领域中模拟多样性的可行性产生了质疑。