  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.
