arXiv:2212.06721cs.LG
The Turing Deception
Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.
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. 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.
You are not alone. This is the litmus test many people are contemplating for a long time now, mostly philosophers, which is not surprising since it is a philosophical question. Most of the heavy stuff is hidden behind paywalls, but here's a nice summary of the state of the art…
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