# The Turing Deception

arXiv: https://arxiv.org/abs/2212.06721 · HTML: https://mlsanity.com/paper/2212.06721/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 9 Dec 2022.

## Builds on (most cited first, 5 of 5)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Release Strategies and the Social Impacts of Language Models](https://mlsanity.com/paper/1908.09203/) (2019)
- [Large Pre-trained Language Models Contain Human-like Biases of What is Right and Wrong to Do](https://mlsanity.com/paper/2103.11790/) (2021)
- [Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses](https://mlsanity.com/paper/1708.07149/) (2017)
- [The Lovelace 2.0 Test of Artificial Creativity and Intelligence](https://mlsanity.com/paper/1410.6142/) (2014)

## Built on by (most cited first, 5 of 5)

- [Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models](https://mlsanity.com/paper/2304.01852/) (2023)
- [Professional Certification Benchmark Dataset: The First 500 Jobs For Large Language Models](https://mlsanity.com/paper/2305.05377/) (2023)
- [ChatGPT as the Transportation Equity Information Source for Scientific Writing](https://mlsanity.com/paper/2303.11158/) (2023)
- [On a Functional Definition of Intelligence](https://mlsanity.com/paper/2312.09546/) (2023)
- [Evaluating AI Vocational Skills Through Professional Testing](https://mlsanity.com/paper/2312.10603/) (2023)

## Abstract (arXiv)

This research revisits the classic Turing test and compares recent large language models such as ChatGPT for their abilities to reproduce human-level comprehension and compelling text generation. Two task challenges -- summarization, and question answering -- prompt ChatGPT to produce original content (98-99%) from a single text entry and also sequential questions originally posed by Turing in 1950. We score the original and generated content against the OpenAI GPT-2 Output Detector from 2019, and establish multiple cases where the generated content proves original and undetectable (98%). The question of a machine fooling a human judge recedes in this work relative to the question of "how would one prove it?" The original contribution of the work presents a metric and simple grammatical set for understanding the writing mechanics of chatbots in evaluating their readability and statistical clarity, engagement, delivery, and overall quality. While Turing's original prose scores at least 14% below the machine-generated output, the question of whether an algorithm displays hints of Turing's truly original thoughts (the "Lovelace 2.0" test) remains unanswered and potentially unanswerable for now.