SemaPop uses LLMs for semantic-conditioned population synthesis, deriving personas from surveys. Integrates with WGAN-GP for statistical alignment and behavioral realism. Achieves better marginal/joint distribution matches with diversity.
Key Points
- 1.Derives personas from surveys using LLMs for semantic-conditioned synthesis
- 2.Integrates WGAN-GP to ensure statistical alignment and behavioral realism
- 3.Outperforms baselines in marginal and joint distribution matching with diversity
Impact Analysis
AI researchers in simulation and social modeling benefit from more realistic synthetic populations. It advances population synthesis by combining semantic understanding with statistical rigor, enabling diverse agent behaviors. This could enhance applications in economics, epidemiology, and policy simulation.
Technical Details
SemaPop employs LLMs to generate semantically conditioned personas from survey data, capturing nuanced attributes. It then uses WGAN-GP to refine distributions, aligning marginals and joints statistically while preserving behavioral realism and diversity over prior methods.