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.

翻译：大语言模型（LLMs）在模拟人类语言使用和行为方面展现出显著潜力。本研究深入探讨了角色变量与大语言模型模拟不同视角能力之间的交叉关系。我们发现，在现有主观自然语言处理数据集的标注中，角色变量可解释的方差不足10%。尽管如此，通过提示工程将角色变量纳入大语言模型仍能带来适度改进。当标注者之间分歧频繁但局限在有限范围内时，角色提示的效果最为显著。存在线性相关性：角色变量对人类标注的影响越大，使用角色提示的大语言模型预测效果就越好。然而，当角色变量的效用较低时（即解释人类标注的方差不足10%），角色提示几乎不产生效果。当前大部分主观NLP数据集属于此类情况，这对当前自然语言处理领域中模拟多样化视角的可行性提出了质疑。