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Turing test

The Turing test is an evaluation method designed to determine whether an artificial intelligence system can exhibit natural language communication that is indistinguishable from that of a human. Proposed by mathematician and computer scientist Alan Turing in 1950 as the imitation game, the standard procedure involves a human evaluator engaging in blind, text-based conversations with both a machine and a human participant. If the evaluator cannot reliably differentiate the artificial system from the human based on conversational responses, linguistic nuance, and reasoning, the machine is deemed to have passed the test. While originally framed as an operational benchmark for machine intelligence, it serves more broadly as a measure of naturalistic communication, behavioral mimicry, and the ability of artificial agents to engage in convincing humanlike interaction.

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Does GPT-4 pass the Turing test?

Does GPT-4 pass the Turing test?

Cameron R. Jones, Ben Bergen

OrganizationsUniversity of California, San Diego

Why you should read this

Demonstrates through a controlled online experiment that GPT-4 achieves a 49.7% success rate against human interrogators in the Turing test, showing that deceptive humanlikeness relies more heavily on persona and linguistic style than raw intelligence.

We evaluated GPT-4 in a public online Turing test. The best-performing GPT-4 prompt passed in 49.7% of games, outperforming ELIZA (22%) and GPT-3.5 (20%), but falling short of the baseline set by human participants (66%). Participants’ decisions were based mainly on linguistic style (35%) and socioemotional traits (27%), supporting the idea that intelligence, narrowly conceived, is not sufficient to pass the Turing test. Participant knowledge about LLMs and number of games played positively correlated with accuracy in detecting AI, suggesting learning and practice as possible strategies to mitigate deception. Despite known limitations as a test of intelligence, we argue that the Turing test continues to be relevant as an assessment of naturalistic communication and deception. AI models with the ability to masquerade as humans could have widespread societal consequences, and we analyse the effectiveness of different strategies and criteria for judging humanlikeness.

Added

2026-09-26