Large language models (LLMs) are trained from vast repositories of text authored by millions of distinct authors, reflecting an enormous diversity of human traits. While these models bear the potential to be used as approximations of human subjects in behavioral studies, prior efforts have been limited in steering model responses to match individual human users. In this work, we introduce "Anthology", a method for conditioning LLMs to particular virtual personas by harnessing open-ended life narratives, which we refer to as "backstories." We show that our methodology enhances the consistency and reliability of experimental outcomes while ensuring better representation of diverse sub-populations. Across three nationally representative human surveys conducted as part of Pew Research Center's American Trends Panel (ATP), we demonstrate that Anthology achieves up to 18% improvement in matching the response distributions of human respondents and 27% improvement in consistency metrics. Our code and generated backstories are available at https://github.com/CannyLab/anthology.

翻译：大型语言模型（LLM）通过训练于数百万不同作者撰写的海量文本库，承载了极其丰富的人类特质多样性。尽管这些模型具备作为人类受试者近似替代应用于行为学研究的潜力，但先前研究在引导模型响应以匹配个体用户特征方面存在局限。本研究提出"Anthology"方法，通过利用开放式人生叙事（即"背景故事"）使LLM适配特定虚拟角色。我们证明该方法在确保更好表征多元子群体的同时，能提升实验结果的连贯性与可靠性。基于皮尤研究中心美国趋势调查组（ATP）开展的三项全国代表性人类调查，我们证实Anthology在匹配人类受访者响应分布方面最高提升18%，在一致性指标上提升27%。相关代码与生成的背景故事已发布于https://github.com/CannyLab/anthology。