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

• URL: http://arxiv.org/abs/2512.00461v1
• Date: Sat, 29 Nov 2025 12:27:34 GMT
• Title: Whose Personae? Synthetic Persona Experiments in LLM Research and Pathways to Transparency
• Authors: Jan Batzner, Volker Stocker, Bingjun Tang, Anusha Natarajan, Qinhao Chen, Stefan Schmid, Gjergji Kasneci,
• Abstract summary: Review of 63 studies published between 2023 and 2025 in leading NLP and AI venues.<n>We show that task and population of interest are often underspecified in persona-based experiments.<n>We introduce a persona transparency checklist that emphasizes representative sampling, explicit grounding in empirical data, and enhanced ecological validity.
• Score: 12.62715115816099
• License: http://creativecommons.org/licenses/by/4.0/
• 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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