“Recent LLMs can clearly fool humans” but so what?

“We evaluated 4 systems (ELIZA, GPT-4o, LLaMa-3.1-405B, and GPT-4.5) in two randomised, controlled, and pre-registered Turing tests on independent populations. Participants had 5 minute 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 they were being compared to -- while baseline models (ELIZA and GPT-4o) achieved win rates significantly below chance (23% and 21% respectively). The results constitute the first empirical evidence that any artificial system passes a standard three-party Turing test. The results have implications for debates about what kind of intelligence is exhibited by Large Language Models (LLMs), and the social and economic impacts these systems are likely to have.”

arxiv.org/abs/2503.23674

SO WHAT?

✅ Still room for quibbles:
✅ Tests were only five minutes
✅ Judges weren't trained

By any measure a striking result: GPT-4.5 passed as human 73% of the time. But so what?

We are once against falling into the ELIZA trap: overattributing intelligence to machines because they are good enough to fool (many) humans. en.wikipedia.org/wiki/ELIZA_effect

via Celebrating the 75th Anniversary of the Turing Test www.youtube.com/live/GmnBTCKocZI

Taken on: October 10, 2025

Tags:

turingtest

Gary-Marcus

gpt

LLM

large-language-model

Alan-turing

artificial intelligence

chat-gpt

chatbot

anthropic

claude

gemini

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