arXiv Artificial Intelligence

MetaPersona: Task-Grounded Synthetic Populations from Empirical Social Science

3 hours ago 01.10.2026 07:00 👁 2 views
MetaPersona: Task-Grounded Synthetic Populations from Empirical Social Science

Quick summary

arXiv:2609.38392v1 Announce Type: new Abstract: 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 re

Key takeaways

  • arXiv:2609.38392v1 Announce Type: new Abstract: 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.

Why it matters

“MetaPersona: Task-Grounded Synthetic Populations from Empirical Social Science” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗