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The use of large language models (LLMs) to simulate human behavior has gained significant attention, particularly through personas that approximate individual characteristics.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Unqovering stereotyping biases via underspecified questions
T. Li, T. Khot, D. Khashabi, A. Sabharwal, and V. Srikumar · 2010
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textblob documentation
S. Loria · 2018
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Language (technology) is power: A critical survey of" bias" in nlp
S. L. Blodgett, S. Barocas, H. Daumé III, and H. Wallach · 2020
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Sync: A copula based framework for generating synthetic data from aggregated sources
Z. Li, Y. Zhao, and J. Fu · 2020
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Gender bias in neural natural language processing
K. Lu, P. Mardziel, F. Wu, P. Amancharla, and A. Datta · 2020
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Bbq: A hand-built bias benchmark for question answering
A. Parrish, A. Chen, N. Nangia, V. Padmakumar, J. Phang, J. Thompson, P. M. Htut, and S. R. Bowman · 2021
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Societal biases in language generation: Progress and challenges
E. Sheng, K.-W. Chang, P. Natarajan, and N. Peng · 2021
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Identifying and manipulating the personality traits of language models
G. Caron and S. Srivastava · 2022
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Generation of synthetic populations in social simulations: a review of methods and practices
K. Chapuis, P. Taillandier, and A. Drogoul · 2022
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Data-driven personas
B. J. Jansen, J. Salminen, S.-g. Jung, and K. Guan · 2022
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Using large language models to simulate multiple humans and replicate human subject studies
G. V. Aher, R. I. Arriaga, and A. T. Kalai · 2023
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Out of one, many: Using language models to simulate human samples
L. P. Argyle, E. C. Busby, N. Fulda, J. R. Gubler, C. Rytting, and D. Wingate · 2023
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S. Feng, C. Y. Park, Y. Liu, and Y. Tsvetkov · 2023
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Should chatgpt be biased? challenges and risks of bias in large language models
E. Ferrara · 2023
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Bias runs deep: Implicit reasoning biases in persona-assigned llms
S. Gupta, V. Shrivastava, A. Deshpande, A. Kalyan, P. Clark, A. Sabharwal, and T. Khot · 2023
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Large language models as simulated economic agents: What can we learn from homo silicus?
J. J. Horton · 2023
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On the humanity of conversational ai: Evaluating the psychological portrayal of llms
J.-t. Huang, W. Wang, E. J. Li, M. H. Lam, S. Ren, Y. Yuan, W. Jiao, Z. Tu, and M. Lyu · 2023
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Generative agents: Interactive simulacra of human behavior
J. S. Park, J. O’Brien, C. J. Cai, M. R. Morris, P. Liang, and M. S. Bernstein · 2023
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In-context impersonation reveals large language models’ strengths and biases
L. Salewski, S. Alaniz, I. Rio-Torto, E. Schulz, and Z. Akata · 2023
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Whose opinions do language models reflect?
S. Santurkar, E. Durmus, F. Ladhak, C. Lee, P. Liang, and T. Hashimoto · 2023
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Character-llm: A trainable agent for role-playing
Y. Shao, L. Li, J. Dai, and X. Qiu · 2023
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Scaling synthetic data creation with 1,000,000,000 personas, 2024
T. Ge, X. Chan, X. Wang, D. Yu, H. Mi, and D. Yu · 2024
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Quantifying the persona effect in llm simulations
T. Hu and N. Collier · 2024
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Applications of gpt in political science research
K. Lee, S. Paci, J. Park, H. Y. You, and S. Zheng · 2024
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Automated social science: Language models as scientist and subjects
B. S. Manning, K. Zhu, and J. J. Horton · 2024
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Generative agent simulations of 1,000 people
J. S. Park, C. Q. Zou, A. Shaw, B. M. Hill, C. Cai, M. R. Morris, R. Willer, P. Liang, and M. S. Bernstein · 2024
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H. Sun, J. Pei, M. Choi, and D. Jurgens · 2023
Cited alongside, same era.
Rolellm: Benchmarking, eliciting, and enhancing role-playing abilities of large language models
Z. M. Wang, Z. Peng, H. Que, J. Liu, W. Zhou, Y. Wu, H. Guo, R. Gan, Z. Ni, J. Yang, et al · 2023
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Expertprompting: Instructing large language models to be distinguished experts
B. Xu, A. Yang, J. Lin, Q. Wang, C. Zhou, Y. Zhang, and Z. Mao · 2023
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Project sid: Many-agent simulations toward ai civilization
A. AL, A. Ahn, N. Becker, S. Carroll, N. Christie, M. Cortes, A. Demirci, M. Du, F. Li, S. Luo, et al · 2024
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Measuring implicit bias in explicitly unbiased large language models
X. Bai, A. Wang, I. Sucholutsky, and T. L. Griffiths · 2024
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Sensitivity, performance, robustness: Deconstructing the effect of sociodemographic prompting
T. Beck, H. Schuff, A. Lauscher, and I. Gurevych · 2024
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Persona: A reproducible testbed for pluralistic alignment, 2024
L. Castricato, N. Lile, R. Rafailov, J.-P. Fränken, and C. Finn · 2024
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Representation bias in political sample simulations with large language models
W. Qi, H. Lyu, and J. Luo · 2024
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Using large language models to generate silicon samples in consumer and marketing research: Challenges, opportunities, and guidelines
M. Sarstedt, S. J. Adler, L. Rau, and B. Schmitt · 2024
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Generating personas using llms and assessing their viability
A. Schuller, D. Janssen, J. Blumenröther, T. M. Probst, M. Schmidt, and C. Kumar · 2024
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Two tales of persona in llms: A survey of role-playing and personalization
Y.-M. Tseng, Y.-C. Huang, T.-Y. Hsiao, Y.-C. Hsu, J.-Y. Foo, C.-W. Huang, and Y.-N. Chen · 2024
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URL https://www.census.gov/data/datasets.html
U.S. Census Bureau, 2024 · 2024
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R. Wang, S. Milani, J. C. Chiu, J. Zhi, S. M. Eack, T. Labrum, S. M. Murphy, N. Jones, K. Hardy, H. Shen, et al · 2024
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Oasis: Open agents social interaction simulations on one million agents
Z. Yang, Z. Zhang, Z. Zheng, Y. Jiang, Z. Gan, Z. Wang, Z. Ling, J. Chen, M. Ma, B. Dong, et al · 2024
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A large-scale empirical study on large language models for election prediction
C. Yu, Z. Weng, Y. Li, Z. Li, X. Hu, and Y. Zhao · 2024
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Bias and toxicity in role-play reasoning
J. Zhao, Z. Qian, L. Cao, Y. Wang, and Y. Ding · 2024
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When” a helpful assistant” is not really helpful: Personas in system prompts do not improve performances of large language models
M. Zheng, J. Pei, L. Logeswaran, M. Lee, and D. Jurgens · 2024
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Civicsync
CivicSync · 2025
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Synthetic users
Synthetic Users · 2025
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