# ChatGPT-4 and other LLMs in the Turing Test: A Critical Analysis

arXiv: https://arxiv.org/abs/2503.06551 · HTML: https://mlsanity.com/paper/2503.06551/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 9 Mar 2025.

## Builds on (most cited first, 3 of 3)

- [People cannot distinguish GPT-4 from a human in a Turing test](https://mlsanity.com/paper/2405.08007/) (2024)
- [Does GPT-4 pass the Turing test?](https://mlsanity.com/paper/2310.20216/) (2023)
- [Human or Not? A Gamified Approach to the Turing Test](https://mlsanity.com/paper/2305.20010/) (2023)

## Built on by (most cited first, 1 of 1)

- [Large Language Models Pass the Turing Test](https://mlsanity.com/paper/2503.23674/) (2025)

## Abstract (arXiv)

This paper critically examines the recent publication "ChatGPT-4 in the Turing Test" by Restrepo Echavarr'ia (2025), challenging its central claims regarding the absence of minimally serious test implementations and the conclusion that ChatGPT-4 fails the Turing Test. The analysis reveals that the criticisms based on rigid criteria and limited experimental data are not fully justified. More importantly, the paper makes several constructive contributions that enrich our understanding of Turing Test implementations. It demonstrates that two distinct formats, the three-player and two-player tests, are both valid, each with unique methodological implications. The work distinguishes between absolute criteria for passing the test--the machine's probability of incorrect identification equals or exceeds the human's probability of correct identification--and relative criteria--which measure how closely a machine's performance approximates that of a human--, offering a more nuanced evaluation framework. Furthermore, the paper clarifies the probabilistic underpinnings of both test types by modeling them as Bernoulli experiments--correlated in the three-player version and uncorrelated in the two-player version. This formalization allows for a rigorous separation between the theoretical criteria for passing the test, defined in probabilistic terms, and the experimental data that require robust statistical methods for proper interpretation. In doing so, the paper not only refutes key aspects of the criticized study but also lays a solid foundation for future research on objective measures of how closely an AI's behavior aligns with, or deviates from, that of a human being.