Song-Ze Yu
Author directory2026
Anamnesis: An Open-Source Platform for Large-Scale Backstory-Conditioned Survey Simulation
Song-Ze Yu | Joseph Suh | Serina Chang | David M. Chan
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Song-Ze Yu | Joseph Suh | Serina Chang | David M. Chan
Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
We present _Anamnesis_, an interactive system for demographically controllable survey simulation using large language models. Open-source and designed for _non-technical_ users and researchers, Anamnesis enables the prototyping and stress-testing of survey instruments on virtual populations. The platform operationalizes the recently introduced Anthology and Alterity frameworks, which use structured narrative backstories to condition model responses, within a unified web interface. It supports open-ended generation, probabilistic demographic resampling, and multimodal (image and audio) surveys. We evaluate the system through two case studies: (1) replicating segments of Pew Research Center’s American Trends Panel (ATP) on political typology and biomedical issues and (2) emulating human preference in the New Yorker Caption Contest. In both cases, _Anamnesis_ produces opinion distributions that more closely match real-world survey data than standard persona-prompting baselines, offering a transparent, reproducible, and open-source alternative to proprietary simulation services.