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  2. GPTZero - Wikipedia

    en.wikipedia.org/wiki/GPTZero

    GPTZero uses qualities it terms perplexity and burstiness to attempt determining if a passage was written by a AI. [14] According to the company, perplexity is how random the text in the sentence is, and whether the way the sentence is constructed is unusual or "surprising" for the application.

  3. Artificial intelligence content detection - Wikipedia

    en.wikipedia.org/wiki/Artificial_intelligence...

    Multiple AI detection tools have been demonstrated to be unreliable in terms of accurately and comprehensively detecting AI-generated text. In a study conducted by Weber-Wulff et al., and published in 2023, researchers evaluated 14 detection tools including Turnitin and GPT Zero, and found that "all scored below 80% of accuracy and only 5 over 70%."

  4. Wikipedia : Wikipedia Signpost/2024-10-19/Recent research

    en.wikipedia.org/wiki/Wikipedia:Wikipedia...

    In more detail, the authors used two existing AI detectors (GPTZero and Binoculars), which "reveal a marked increase in AI-generated content in recent[ly created] pages compared to those from before the release of GPT-3.5 [in March 2022].

  5. Undetectable.ai - Wikipedia

    en.wikipedia.org/wiki/Undetectable.ai

    Undetectable AI (or Undetectable.ai) is an artificial intelligence content detection and modification software designed to identify and alter artificially generated text, such as that produced by large language models.

  6. Hallucination (artificial intelligence) - Wikipedia

    en.wikipedia.org/wiki/Hallucination_(artificial...

    Plagiarism detectors gave the generated articles an originality score of 100%, meaning that the information presented appears to be completely original. Other software designed to detect AI generated text was only able to correctly identify these generated articles with an accuracy of 66%.

  7. Generative pre-trained transformer - Wikipedia

    en.wikipedia.org/wiki/Generative_pre-trained...

    Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.