arXiv AI
Data-Driven Personas for Survey Simulation: Insights into Simulation Alignment Across Data-Access Regimes
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Sep 2
Data-Driven Persona-Conditioned Agents for A/B Test Simulation
The paper introduces a simulation framework that uses large language model (LLM) agents conditioned on data‑driven personas to predict A/B test outcomes. These personas are built from anonymized user behavioral patterns, engagement signals, and inferred demographics, offering a more realistic population model than synthetic or rule‑based personas. The authors evaluate question design, persona data source, behavioral depth versus diversity, and population subsampling, achieving 0.75–0.90 directional accuracy on 40 real A/B tests.
arXiv Computation and Language
Sep 3
When Persona Attributes Improve Population Alignment in Large Language Models
The paper investigates how persona prompting—using short textual descriptions of individuals—to align large language models (LLMs) with human survey responses. It examines the impact of selecting different persona attributes and finds that not all attribute combinations improve performance, suggesting that the variation in human responses to survey questions may explain mixed results. The study evaluates multiple attribute selection methods across four social surveys, two countries, six LLMs, and twenty prediction tasks, offering guidance on when persona prompting is beneficial and which attribute choices are most effective.
arXiv AI
Jul 31
When Synthetic Users Fail: A Cross-Domain Benchmark of LLM-Simulated Human Survey Responses
arXiv:2607. 26348v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as synthetic users, stand-ins for human respondents whose simulated answers feed product, policy, and market decisions.
arXiv AI
Aug 5
Emulate or Estimate? The Divergent Strengths of Base and Post-Trained Language Models for Opinion Simulation
arXiv:2608. 03044v1 Announce Type: cross Abstract: Large language models are increasingly used to simulate human opinions, but prior work reports conflicting results: some studies find promising alignment with human survey data, while others find persona collapse and weak demographic sensitivity.
arXiv AI
Jun 18
How Well Do Large Language Models Capture Human Personality?
arXiv:2606. 18263v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to simulate human populations via persona prompting, often under the assumptions that richer persona descriptions improve behavioral fidelity, similarly sized attribute combinations are equally simulatable, and persona definitions generalize across tasks.
arXiv AI
Aug 25
When Persona Simulations Are Informative: Graph-Structured Signals for Pluralistic Opinion Sensing
arXiv:2608.22438v1 Announce Type: new Abstract: Persona-conditioned large language models (LLMs) are increasingly used to simulate survey responses across diverse domains. However, apparent response...