The Turing Deception

• URL: http://arxiv.org/abs/2212.06721v1
• Date: Fri, 9 Dec 2022 16:32:11 GMT
• Title: The Turing Deception
• Authors: David Noever, Matt Ciolino
• Abstract summary: This research revisits the classic Turing test and compares recent large language models such as ChatGPT. The question of whether an algorithm displays hints of Turing's truly original thoughts remains unanswered and potentially unanswerable for now.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. 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.

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