Using large language models in psychology
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LLMs have demonstrated the capability to generate human-like experimental samples in studies from political science, psychology, and behavioral science.
“Their experiments have demonstrated the capability of LLMs to generate human-like experimental samples in studies from political science [9, 11], psychology [29, 97], and behavioral science [11].”
From AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society (2025) · cites this paper as [29]
Expert validation remains essential, especially as LLMs are increasingly integrated into research systems.
“Nonetheless, expert validation remains essential 40,41 , especially as LLMs are increasingly integrated into research systems 42-44 .”
From Quantifying the prevalence and impact of overreaching causal claims in social science (2026) · cites this paper as [43]
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You are what you're for: Essentialist categorization in large language modelsPreprint
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Selective Explanations: Leveraging Human Input to Align Explainable AIPreprint
Lai, V., Zhang, Y., Chen, C., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2301.09656 · Cited by 3 · see citing works
OpenAI, Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.08774
Self-citation
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Artificial intelligence and illusions of understanding in scientific research
Messeri, L., & Crockett, M. J. (2024) · Nature · doi:10.1038/s41586-024-07146-0 · Cited by 707 · see citing works
Cultural bias and cultural alignment of large language models
Tao, Y., Viberg, O., Baker, R. S., & Kizilcec, R. F. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae346 · Cited by 383 · see citing works
GPT is an effective tool for multilingual psychological text analysis
Rathje, S., Mirea, D.-M., Sucholutsky, I., Marjieh, R., Robertson, C. E., & Van Bavel, J. J. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2308950121 · Cited by 297 · see citing works
Moore, J., Grabb, D., Agnew, W., Klyman, K., Chancellor, S., Ong, D. C., & Haber, N. (2025) · Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3715275.3732039 · Cited by 204 · see citing works
Self-citationSarstedt, M., Adler, S. J., Rau, L., & Schmitt, B. (2024) · Psychology & Marketing · doi:10.1002/mar.21982 · Cited by 149 · see citing works
Using natural language processing to analyse text data in behavioural science
Feuerriegel, S., Maarouf, A., Bär, D., Geissler, D., Schweisthal, J., Pröllochs, N., Robertson, C. E., Rathje, S., Hartmann, J., Mohammad, S. M., et al. (2025) · Nature Reviews Psychology · doi:10.1038/s44159-024-00392-z · Cited by 140 · see citing works
Perils and opportunities in using large language models in psychological research
Abdurahman, S., Atari, M., Karimi-Malekabadi, F., Xue, M. J., Trager, J., Park, P. S., Golazizian, P., Omrani, A., & Dehghani, M. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae245 · Cited by 111 · see citing works
Harnessing the potential of large language models in medical education: promise and pitfalls
Benítez, T. M., Xu, Y., Boudreau, J. D., Kow, A. W. C., Bello, F., Van Phuoc, L., Wang, X., Sun, X., Leung, G. K.-K., Lan, Y., et al. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad252 · Cited by 100 · see citing works
Introduction to large language models (LLMs) for dementia care and research
Treder, M. S., Lee, S., & Tsvetanov, K. A. (2024) · Frontiers in Dementia · doi:10.3389/frdem.2024.1385303 · Cited by 63 · see citing works
Lehr, S. A., Caliskan, A., Liyanage, S., & Banaji, M. R. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2404328121 · Cited by 66 · see citing works
Assessing and understanding creativity in large language models
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Habicht, J., Dina, L.-M., McFadyen, J., Stylianou, M., Harper, R., Hauser, T. U., & Rollwage, M. (2025) · Journal of Medical Internet Research · doi:10.2196/60435 · Cited by 52 · see citing works
Generative artificial intelligence–mediated confirmation bias in health information seeking
Lopez‐Lopez, E., Abels, C. M., Holford, D., Herzog, S. M., & Lewandowsky, S. (2025) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.15413 · Cited by 38 · see citing works
Evaluating generative AI responses to real-world drug-related questions
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Reimagining flipped learning via bloom's taxonomy and student–teacher–GenAI interactions
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Li, J. Z., Herderich, A., & Goldenberg, A. (2024) · doi:10.31234/osf.io/fzvd8 · Cited by 10 · see citing works
Characterizing Delusional Spirals through Human-LLM Chat Logs
Moore, J., Mehta, A., Agnew, W., Anthis, J. R., Louie, R., Mai, Y., Yin, P., Cheng, M., Paech, S. J., Klyman, K., et al. (2026) · Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3805689.3806443 · Cited by 15 · see citing works
Self-citationConformity in large language models
Zhu, X., Zhang, C., Stafford, T., Collier, N., & Vlachos, A. (2025) · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2025.acl-long.195 · Cited by 10 · see citing works
Expressions of uncertainty in online science communication hinder information diffusion
Stavrova, O., Kleinberg, B., Evans, A. M., & Ivanović, M. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae439 · Cited by 9 · see citing works
LLM-based System for Technical Writing Real-time Review in Urban Construction and Technology
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Judgments of learning distinguish humans from large language models in predicting memory
Huff, M., & Ulakci, E. (2025) · Scientific Reports · doi:10.1038/s41598-025-22290-x · Cited by 9 · see citing works
Quantifying the prevalence and impact of overreaching causal claims in social science
Isch, C., Dörr, T., Fasching, N., Jennings, G., & Watts, D. J. (2026) · Nature Human Behaviour · doi:10.1038/s41562-026-02553-x
Understanding large language models demands distinguishing human projection from machine cognition
Li, L., Teng, Y., Wang, Y., & Hu, X. (2026) · Communications Psychology · doi:10.1038/s44271-026-00508-6
Conformity in Large Language Models
Zhu, X., Zhang, C., Stafford, T., Collier, N., & Vlachos, A. (2024) · arXiv · doi:10.48550/arxiv.2410.12428
“Stereotypes, biases, and other human-like behaviors are shaped by the vast amounts of training data used in pre-training (Ke et al., 2024; Demszky et al., 2023).”
Piao, J., Yan, Y., Zhang, J., Li, N., Yan, J., Lan, X., Lu, Z., Zheng, Z., Wang, J. Y., Zhou, D., et al. (2025) · arXiv · doi:10.48550/arxiv.2502.08691 · Cited by 43 · see citing works
“Recently, some researchers have attempted to use LLMs as “silicon samples” for social experiments [11, 9, 86, 93, 4, 28].”
- Authors
- Demszky, D., Yang, D., Yeager, D. S., Bryan, C. J., Clapper, M., Chandhok, S., Eichstaedt, J. C., Hecht, C., Jamieson, J., Johnson, M., Jones, M., Krettek-Cobb, D., Lai, L., JonesMitchell, N., Ong, D. C., Dweck, C. S., Gross, J. J., & Pennebaker, J. W.
- Affiliations
- Stanford University (US)
- The University of Texas at Austin (US)
- Google (United States) (US)
- University of Rochester (US)
- Corresponding author
- Demszky, Dorottya
- Yang, Diyi
- Yeager, David S.
- ORCID
- Venue
- Nature Reviews Psychology
- Published
- 2023-10-13
- ISSN
- 2731-0574 (ISSN-L)
- OpenAlex
- W4387617694
- Semantic Scholar
- 067cf6cba7aee45d75c8ef31fbda3e7b8cfaaad1
- Cited by
- 369
- References
- 94
- Citations by year
- 2026: 86 · 2025: 160 · 2024: 109 · 2023: 13
- FWCI
- 7.68
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Reviews Psychology (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- large language models, GPT-4, llamas, psychological measurement, language data analysis, benchmark
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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