ArticleProceedings of the National Academy of Sciences of the United States of America2026
Large language models pass a standard three-party Turing test.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Human-like conversational agents as social partners: a scoping review of socioaffective mechanisms, well-being outcomes, risks and governance in the post-Turing era.Frontiers in artificial intelligence · 2026Pooled it
- Review
- Brain-AI convergence: Generative world models and hierarchical attention for human intelligence.Patterns (New York, N.Y.) · 2026Review
- Simulated self-assessment in large language models: A psychometric approach to AI self-efficacy.Computers in human behavior. Artificial humans · 2026Article
- Persuading large language models to comply with objectionable requests.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Large language models pass a standard three-party Turing test.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Article
- Evaluating the utility of large language models for detecting and simulating language dysfunction.Frontiers in artificial intelligence · 2026Article
- Metacognition of ChatGPT in confidence judgements.Frontiers in artificial intelligence · 2026Article
- Techno-emotional projection in human-GenAI relationships: a psychological and ethical conceptual perspective.Frontiers in psychology · 2025Review
- Gödelian embodied self-referential genomic intelligence: lessons for AI and AGI from the genomic blockchain.Frontiers in robotics and AI · 2025Article
- Mutual human-robot understanding for a robot-enhanced society: the crucial development of shared embodied cognition.Frontiers in artificial intelligence · 2025Review
- An Introduction to Generative Artificial Intelligence for Academics.F1000Research · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The Turing test has been widely discussed as a test of machine intelligence, but it also provides a measure of how humans distinguish other humans from machines. We evaluated 4 systems (ELIZA, GPT-4o, LLaMa-3.1-405B, and GPT-4.5) in two randomized, controlled, and preregistered Turing tests on independent populations. Participants had 5 min conversations simultaneously with another human participant and one of these systems before judging which conversational partner they thought was human. When prompted to adopt a humanlike persona, GPT-4.5 was judged to be the human 73% of the time: significantly more often than interrogators selected the real human participant. LLaMa-3.1, with the same prompt, was judged to be the human 56% of the time-not significantly more or less often than the humans it was being compared to. Without these prompts, however, the same models performed significantly worse (38% and 36%), and did not consistently outperform baseline models, ELIZA and GPT-4o (23% and 21%, respectively). A third study replicated these results in 15-min games: two PERSONA-prompted models achieved pass rates of 56% and 59%. The results constitute empirical evidence that artificial systems can pass a standard three-party Turing test. Interrogators' reasoning focused more on stylistic and socio-emotional aspects of human behavior rather than more traditional notions of intelligence. The results have implications for debates about what kind of intelligence is exhibited by large language models, the social impacts these systems are likely to have, and the aspects of human behavior that people continue to see as unique.
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Registered trials
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.