Sign inGet started
Paper · arXiv

MetaPersona: Task-Grounded Synthetic Populations from Empirical Social Science

Personas used to seed LLM social simulations face a cold-start problem: existing methods lack a principled basis for deciding which attributes to include and how to assign their values. As a result, synthetic populations may misrepresent the demographic composition, latent attributes, and dependency structure that shape downstream behavior. We introduce MetaPersona-DB, a dataset of 11,000+ empirical human-subjects studies annotated with task-relevant variables, reported relationships, and aggregate-level population statistics. Building on this resource, we propose MetaPersona, a framework that retrieves task-relevant evidence, constructs literature-derived persona dependency graphs, and s...

AuthorsJinyi Ye, Yuangang Li, Chenxiao Yu et al.
Primary categorycs.AI
Published29 September 2026
Last updated at source29 September 2026
cs.AI
Embed this entry
<iframe src="https://silytics.si/embed/item/bae18b7e-fc55-4982-8260-d2f8d1cfe08e" width="420" height="190" style="border:0" loading="lazy" title="Silytics entry"></iframe>

Free, no tracking. More about feeds and embeds.

Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.