Contemporary research in social sciences is increasingly utilizing state-of-the-art statistical language models to annotate or generate content. While these models perform benchmark-leading on common language tasks and show exemplary task-independent emergent abilities, transferring them to novel out-of-domain tasks is only insufficiently explored. The implications of the statistical black-box approach - stochastic parrots - are prominently criticized in the language model research community; however, the significance for novel generative tasks is not. This work investigates the alignment between personalized language models and survey participants on a Moral Foundation Theory questionnaire. We adapt text-to-text models to different political personas and survey the questionnaire repetitively to generate a synthetic population of persona and model combinations. Analyzing the intra-group variance and cross-alignment shows significant differences across models and personas. Our findings indicate that adapted models struggle to represent the survey-captured assessment of political ideologies. Thus, using language models to mimic social interactions requires measurable improvements in in-context optimization or parameter manipulation to align with psychological and sociological stereotypes. Without quantifiable alignment, generating politically nuanced content remains unfeasible. To enhance these representations, we propose a testable framework to generate agents based on moral value statements for future research.

本研究解决了现有语言模型在多样化政治任务中的适配性不足的问题。通过将文本生成模型与道德基础理论问卷对齐，我们发现现有模型在表现政治意识形态时的不足，提出了一种可测试的框架以改进基于道德价值声明的代理生成。这项工作预示着在社会互动模拟中的重要改进需求，促进未来生成更加政治细致的内容。

朝向“差异化人工智能心理学”及与道德基础理论的背景价值驱动声明对齐