Deploying large language models (LLMs) with agency in real-world applications raises critical questions about how these models will behave. In particular, how will their decisions align with humans when faced with moral dilemmas? This study examines the alignment between LLM-driven decisions and human judgment in various contexts of the moral machine experiment, including personas reflecting different sociodemographics. We find that the moral decisions of LLMs vary substantially by persona, showing greater shifts in moral decisions for critical tasks than humans. Our data also indicate an interesting partisan sorting phenomenon, where political persona predominates the direction and degree of LLM decisions. We discuss the ethical implications and risks associated with deploying these models in applications that involve moral decisions.

本研究解决了大型语言模型（LLM）在面对道德困境时如何与人类判断对齐的问题，特别是不同社会人口统计特征的个性角色对决策的影响。研究发现，LLM的道德决策因个性而异，在关键任务上表现出比人类更大的变化，同时揭示了政治个性对LLM决策方向和程度的影响。这一发现对在涉及道德决策的应用中部署这些模型的伦理风险具有重要意义。

探索个性化大型语言模型在道德机器实验中的对齐