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  2. 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%."

  3. 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.

  4. 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.

  5. Content similarity detection - Wikipedia

    en.wikipedia.org/wiki/Content_similarity_detection

    A study was conducted to test the effectiveness of similarity detection software in a higher education setting. One part of the study assigned one group of students to write a paper. These students were first educated about plagiarism and informed that their work was to be run through a content similarity detection system.

  6. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    When assessing the quality of a vector model, a user may draw on this accuracy test which is implemented in word2vec, [28] or develop their own test set which is meaningful to the corpora which make up the model. This approach offers a more challenging test than simply arguing that the words most similar to a given test word are intuitively ...

  7. Naive Bayes spam filtering - Wikipedia

    en.wikipedia.org/wiki/Naive_Bayes_spam_filtering

    Each word in the email contributes to the email's spam probability, or only the most interesting words. This contribution is called the posterior probability and is computed using Bayes' theorem . Then, the email's spam probability is computed over all words in the email, and if the total exceeds a certain threshold (say 95%), the filter will ...