arXiv AI By Zonghan Li, Feng Ji

Statistical realism is not evidence that LLMs can estimate treatment effects in social science experiments

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arXiv:2604. 02458v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used to simulate human responses and estimate treatment effect of interventions when real-world experiments are costly or infeasible.

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arXiv AI
Jun 12

Automated reproducibility assessments in the social and behavioral sciences using large language models

arXiv:2606. 13670v1 Announce Type: new Abstract: Reproducibility in the social and behavioral sciences is typically evaluated by independent researchers who reanalyze the original data to assess whether the published findings can be recovered.

By Tobias Holtdirk, Pietro Marcolongo, Anna Steinberg Schulten, Felix Henninger, Stefan Rose, Sarah Ball, Bolei Ma, Frauke Kreuter, Markus Weinmann, Stefan Feuerriegel
arXiv Computation and Language
Sep 23

Do Synthetic Personas Predict Real Audience Response? A Sim-to-Real Study Where a No-Persona Baseline Beats Persona-Based Copy Simulation

The study evaluates whether large language models (LLMs) used as synthetic personas can predict real audience responses to marketing copy. Using thousands of headline A/B tests from the Upworthy Research Archive, the authors compare a ten-persona panel grounded in real audience demographics to a no-persona zero‑shot baseline that asks the model for a typical reader’s click likelihood. Results show that the no‑persona baseline outperforms the persona‑based approach, with higher predictive validity and top‑1 accuracy, indicating that forcing the model to role‑play specific personas introduces bias and noise.

By Alexandre Cristov\~ao Maiorano