Twin-2K-500
ReleasedA large dataset for building and evaluating digital twins based on the responses of real people.
Background
Digital twins aim to simulate the behavior and preferences of specific people. Twin-2K-500 supports large-scale study of when these simulations preserve genuine individual variation and when they collapse toward stereotypes or population averages.
What it evaluates
- Individual-level response prediction
- Relative heterogeneity across people
- Fidelity across different subject domains
- Bias introduced by richer persona descriptions
Dataset
The dataset contains responses from more than 2,000 people to more than 500 questions. Follow-on evaluation spans 19 domains.
Evaluation methodology
A model receives persona information and predicts held-out responses for the corresponding participant. Analysis compares aggregate accuracy with the more demanding goal of preserving individual-level behavior and variation.
Representative task
Given a participant’s available background and prior responses, predict how that same participant will answer a held-out question.
Results
Published findings show that digital twins can capture relative heterogeneity but still struggle with precise individual prediction and systematic persona-induced bias.