The paper investigates how persona prompting—using short textual descriptions of individuals—to align large language models (LLMs) with human survey responses. It examines the impact of selecting different persona attributes and finds that not all attribute combinations improve performance, suggesting that the variation in human responses to survey questions may explain mixed results. The study evaluates multiple attribute selection methods across four social surveys, two countries, six LLMs, and twenty prediction tasks, offering guidance on when persona prompting is beneficial and which attribute choices are most effective.
By Leon Fr\"ohling, Jens Rupprecht, Markus Strohmaier, Claudia Wagner
arXiv:2610.05828v2 Announce Type: replace
Abstract: Large language models (LLMs) offer new opportunities for public opinion research by enabling early prediction of survey responses, potentially redu...
By Dongryeol Lee, Weronika {\L}ajewska, Leonardo Perelli, Saab Mansour
arXiv:2608.22438v1 Announce Type: new
Abstract: Persona-conditioned large language models (LLMs) are increasingly used to simulate survey responses across diverse domains. However, apparent response...
By Taehyeon An, Jaehyeong Park, Donghyuk Shin
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
arXiv:2610.03998v2 Announce Type: replace
Abstract: Large language models (LLMs) are increasingly used as synthetic personas representing survey respondents. Their validity as substitutes for particu...
By Khashayar Pourtaheri, Ahmad Zareei
arXiv:2608.20344v1 Announce Type: new
Abstract: LLM-based "digital twins" aim to simulate how an individual would behavein new environments or respond to novel questions, given some representation of...
By Iris Ye, Tianze Deng, Ozan Candogan
The paper introduces a simulation framework that uses large language model (LLM) agents conditioned on data‑driven personas to predict A/B test outcomes. These personas are built from anonymized user behavioral patterns, engagement signals, and inferred demographics, offering a more realistic population model than synthetic or rule‑based personas. The authors evaluate question design, persona data source, behavioral depth versus diversity, and population subsampling, achieving 0.75–0.90 directional accuracy on 40 real A/B tests.
By Ziyad Benomar, Weronika {\L}ajewska, Leonardo Perelli, Saab Mansour
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.
By Woojung Song, Dongmin Choi, Yoonah Park, Jongwook Han, Eun-Ju Lee, Yohan Jo
arXiv:2608.22582v1 Announce Type: cross
Abstract: Large-scale population surveys are essential for generating robust social and scientific insights, yet they face significant challenges, including de...
By Julia Romberg, Tobias Gummer, Gabriella Lapesa, Tanja Kunz, Claudia Wagner
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...
By Daehwan Ahn, Chengfeng Mao, Dokyun Lee
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.
By Aanisha Bhattacharyya, Yaman Kumar Singla, Rajiv Ratn Shah, Changyou Chen, Jitendra Ajmera
The paper introduces ASURRE, a benchmark dataset for detecting AI‑assisted responses in online surveys. It evaluates how different LLM usage strategies—ranging from full generation to persona‑grounded agentic completion—affect the performance of existing machine‑generated text detectors. The study finds that while naive AI usage is easily detected, more sophisticated persona‑grounded agents approach chance performance, yet still leave identifiable behavioural traces that can be aggregated to improve detection.
By Qizhou Wang, Bogdan Mamaev, Christopher Leckie