arXiv AI
Jun 12
LLMs Can Better Capture Human Judgments--With the Right Prompts
arXiv:2606. 12754v1 Announce Type: cross Abstract: Are large language models (LLMs) bad at capturing human judgment?
Beyond the Mean: Three-Axis Fidelity for Aligning LLM-Based Survey Simulators from Small Pilot Data
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.
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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
Jul 7
Silicon Sampling via Cross-Survey Transfer
arXiv:2607. 03091v1 Announce Type: new Abstract: Silicon sampling-using large language models (LLMs) to simulate human survey respondents-has emerged as a promising approach for augmenting traditional survey research.
arXiv AI
Jul 24
More Is Not More: What Matters for Diversity in LLM Opinions?
arXiv:2607. 20429v1 Announce Type: cross Abstract: Large language models are increasingly used to simulate diverse human opinions in open-ended tasks such as synthetic surveys, focus group modeling, and public opinion prediction.
arXiv AI
Jul 28
Statistical realism is not evidence that LLMs can estimate treatment effects in social science experiments
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.
arXiv AI
Jul 24
Response drift across frontier large language models
arXiv:2607. 20454v1 Announce Type: cross Abstract: All frontier large language models (LLMs) exhibit response drift -- producing outputs that deviate from expert-validated references -- yet the magnitude and structure of this drift remain uncharacterised by systematic human evaluation.
arXiv AI
Jun 17
Statistical Foundations of LLM-based A/B Testing: A Surrogacy Framework for Human Causal Inference
arXiv:2606. 17165v1 Announce Type: cross Abstract: Organizations and researchers show increasing interest in using large language models (LLMs) in place of human participants in A/B tests, in the hope of experimenting faster and at lower cost.
arXiv AI
Jun 30
LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals
arXiv:2411. 10109v3 Announce Type: replace Abstract: Machine learning can predict human behavior well when substantial structured data are available for well-defined outcomes.
arXiv AI
Jun 17
Patients With Personality: Realistic Patient Simulation through Controlled Diversity and Selective Disclosure
arXiv:2606. 17441v1 Announce Type: cross Abstract: Simulating realistic patient interactions is a key requirement to testing clinical applications of LLMs at scale without time-consuming and expensive user studies.
arXiv AI
Jun 9
From `May' to `Is': Certainty Distortion in Language Model Rewriting
arXiv:2606. 07951v1 Announce Type: cross Abstract: Humans increasingly turn to Language Models (LMs) in ways that shape beliefs and drive decisions, including discussing, rewriting, and summarizing information from scientific articles, news, and medical reports.
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.