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.
By Zihan Chen, Di Zhu, Lei Nico Zheng
The study compares human deliberation in Wason selection tasks with large language model (LLM) agent groups that are seeded with participants’ pre-discussion beliefs. Across various scoring definitions, human consensus rates ranged from 24.0% to 57.0%, whereas LLM agents consistently achieved higher consensus, with gaps of 34–44 percentage points in two sensitivity analyses. Even when early stopping was removed or memorizable answers were eliminated, LLM groups still reached near-unanimous agreement, often on incorrect answers, indicating that simulated consensus does not reflect collective accuracy.
By Tengfei Shao
arXiv:2606. 28963v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to simulate social survey responses, yet their outputs exhibit systematic biases: marginal distributions are skewed, response variance is poorly calibrated, and predictor-outcome relationships are attenuated.
By Eun Cheol Choi, Youngrae Kim, Prabhu Pugalenthi, Hong-En Chen, Bo-Ruei Huang
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.
By Zonghan Li, Feng Ji
arXiv:2609.25066v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly used to simulate human survey responses and behavioral reactions, yet unreliable simulations can mislead...
By Pei Wang, Lei Wang, Yuanzi Li, Xu Chen
The paper introduces Population Fidelity, an evaluation framework for assessing how well large language models (LLMs) represent human population attitudes. It focuses on three dimensions: group-level accuracy, between-group variation, and the structure of that variation. Using the framework, the authors replicate a prior study on machine bias and test cultural fine-tuning, finding that while fine-tuning improves overall alignment, it does not enhance representation of within-population differences.
By Neemias B. da Silva, Martin Lukk, Ali Sutani, Abhishek Moturu, Harris Yang, Daniel Silver, Matt Ratto, Thiago H. Silva