arXiv AI
Behavioral History Outperforms Descriptions of the Person for LLM Synthetic Personas
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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
Aug 18
Plausible but Not Valid: A Psychometric Audit of LLMs as Synthetic Survey Respondents
arXiv:2608. 14606v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as synthetic survey respondents, but existing evaluations ask whether answers look plausible at the individual level.
arXiv Computation and Language
Sep 22
Recognition, Simulation, and Refusal: A Contamination-Aware Study of Classic Psychological Effects in LLM Agents
arXiv:2609.22090v1 Announce Type: new Abstract: An LLM producing the response pattern associated with a human psychological effect is not the same claim as the LLM possessing that bias. We present Ps...
arXiv Machine Learning
Sep 1
Item-Mean Surrogates: Why Richer Persona Data Fail to Improve LLMs as Human Surrogates
arXiv:2608.29455v1 Announce Type: cross Abstract: LLMs are increasingly used as human surrogates, often on the premise that richer persona data could make them substitutes or exploratory tools for sp...
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.
arXiv AI
Sep 4
Human Psychometric Questionnaires Mischaracterize LLM Behavior
The paper investigates whether human psychometric questionnaires can reliably characterize large language models (LLMs) in everyday interactions. By comparing eight open‑source LLMs’ value and personality profiles from Likert self‑reports (PVQ‑40/21 and BFI‑44/10) with generation probabilities on value‑laden user queries, the authors find substantial divergence between the two methods. The study shows that questionnaire items contain explicit lexical cues that lead models to respond in socially desirable ways, whereas realistic user queries lack such cues, and demographic persona prompts shift questionnaire responses but not generation outputs, indicating that questionnaire scores overestimate LLMs’ true behavioral tendencies.