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  3. A Large Language Model Framework For Sample-free Population Synthesis.
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  3. A Large Language Model Framework For Sample-free Population Synthesis.

Related Experiment Video

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

A large language model framework for sample-free population synthesis.

Michael Jones1, Richard Dawson1, Jon Mills1

  • 1School of Engineering, Newcastle University, Newcastle, United Kingdom.

Plos One
|June 2, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel framework using large language models (LLMs) to create synthetic populations for agent-based models without needing microdata. The LLM-based approach generates accurate, household-structured populations from aggregate data, enhancing accessibility for data-constrained research.

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

  • Computational Social Science
  • Demographic Modeling
  • Artificial Intelligence Applications

Background:

  • Agent-based models (ABMs) require realistic synthetic populations for accurate simulations in diverse fields.
  • Existing population synthesis methods often depend on scarce, privacy-restricted, or coarse-scale census microdata.
  • This limitation restricts the application of ABMs in data-scarce environments.

Purpose of the Study:

  • To present a sample-free framework for generating complete, household-structured synthetic populations using large language models (LLMs).
  • To enable the creation of detailed demographic representations directly from aggregate data, overcoming microdata limitations.
  • To expand the utility of ABMs in research settings with constrained data availability.

Main Methods:

  • A multi-step, LLM-agnostic framework involving objective definition, input preparation, LLM selection, and synthetic household generation.
  • Population synthesis is achieved through iterative prompting, where the LLM generates households guided by discrepancies between synthetic and target distributions.
  • The method leverages the LLM's pre-trained knowledge for plausible attribute combinations, ensuring statistical alignment and structural feasibility without model fine-tuning.

Main Results:

  • Global evaluation across 109 countries demonstrated high accuracy in reproducing marginal distributions like gender (SRMSE: 0.003) and household size (SRMSE: 0.026).
  • Complex attributes such as household composition (SRMSE: 0.062) and age (SRMSE: 0.128) were also accurately reproduced.
  • Case studies in Newcastle upon Tyne (UK) and Dar es Salaam (Tanzania) validated the framework's effectiveness.

Conclusions:

  • The developed framework successfully generates coherent, household-structured synthetic populations from aggregate data, eliminating the need for microdata.
  • This approach significantly enhances the applicability of agent-based modeling in diverse research areas facing data limitations.
  • The LLM-based method offers an accessible and low-data-requirement solution for constructing foundational demographic datasets for simulations.