Virtual Personas for Language Models via an Anthology of Backstories

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 subpopulations. 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 is available at https://github.com/CannyLab/anthology . A: I'm 37. I grew up in a small town, in a small house … A: Certainly! I am a new college grad from New Jersey … A: Born and raised in Tennessee, I had a blissful childhood … Step 1. LLM-Generation of Backstories Step 3. Match Virtual Personas to Target Human User Distribution Match to Human User Distribution (Demographic Variables) Step 2. Demographic Survey on Virtual Personas Q: What is your age? (a) 18-29 (d) 65 or above (b) 30-49 (d) Prefer not to answer (c) 50-64 A: (b) 37 years old. Backstory Conditioned Virtual Persona Q: What is the highest level of education you have completed ? (a) Less than high school … … A: (e) Bachelor's degree LLM Q: Tell me about yourself.