ArticleProceedings of the National Academy of Sciences of the United States of America2025
Take caution in using LLMs as human surrogates.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- AI cannot replace human participants in behavioral research.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Digital twins are funhouse mirrors: Five systematic distortions.Science advances · 2026Article
- Understanding large language models demands distinguishing human projection from machine cognition.Communications psychology · 2026Review
- A validity-guided workflow for robust large language model research in psychology.Behavior research methods · 2026Article
- Article
- Persuading large language models to comply with objectionable requests.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Conversational AI and Personal Growth: Insights from a Critical Integrative Review.Behavioral sciences (Basel, Switzerland) · 2026Review
- A large-scale evaluation of commonsense knowledge in humans and large language models.PNAS nexus · 2026Article
- Snake Oil or Panacea? How to Misuse AI in Scientific Inquiries of the Human Mind.Behavioral sciences (Basel, Switzerland) · 2026Review
- Evaluating Generative AI Psychotherapy Chatbots Used by Youth: Cross-Sectional Study.JMIR mental health · 2025Article
- Counterfeit judgments in large language models.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- The simulation of judgment in LLMs.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
Recent studies suggest large language models (LLMs) can generate human-like responses, aligning with human behavior in economic experiments, surveys, and political discourse. This has led many to propose that LLMs can be used as surrogates or simulations for humans in social science research. However, LLMs differ fundamentally from humans, relying on probabilistic patterns, absent the embodied experiences or survival objectives that shape human cognition. We assess the reasoning depth of LLMs using the 11-20 money request game. Nearly all advanced approaches fail to replicate human behavior distributions across many models. The causes of failure are diverse and unpredictable, relating to input language, roles, safeguarding, and more. These results warrant caution in using LLMs as surrogates or for simulating human behavior in research.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.