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arXiv · 2512.00461

Whose Personae? Synthetic Persona Experiments in LLM Research and Pathways to Transparency

Jan Batzner·Volker Stocker·Bingjun Tang·Anusha Natarajan·Qinhao Chen·Stefan Schmid·Gjergji Kasneci
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Abstract

Synthetic personae experiments have become a prominent method in Large Language Model alignment research, yet the representativeness and ecological validity of these personae vary considerably between studies. Through a review of 63 peer-reviewed studies published between 2023 and 2025 in leading NLP and AI venues, we reveal a critical gap: task and population of interest are often underspecified in persona-based experiments, despite personalization being fundamentally dependent on these criteria. Our analysis shows substantial differences in user representation, with most studies focusing on limited sociodemographic attributes and only 35% discussing the representativeness of their LLM personae. Based on our findings, we introduce a persona transparency checklist that emphasizes representative sampling, explicit grounding in empirical data, and enhanced ecological validity. Our work provides both a comprehensive assessment of current practices and practical guidelines to improve the rigor and ecological validity of persona-based evaluations in language model alignment research.

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Jan Batzner, Volker Stocker, Bingjun Tang, Anusha Natarajan, Qinhao Chen, Stefan Schmid, Gjergji Kasneci. 2025-11-29. Whose Personae? Synthetic Persona Experiments in LLM Research and Pathways to Transparency. https://doi.org/10.1609/aies.v8i1.36553

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