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  2. Wikipedia : WikiProject AI Cleanup

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

    Identifying AI-assisted edits is difficult in most cases since the generated text is often indistinguishable from human text. Some exceptions are if the text contains phrases like "as an AI model" or "as of my last knowledge update" and if the editor copy-pasted the prompt used to generate the text together with the AI response.

  3. QuillBot - Wikipedia

    en.wikipedia.org/wiki/QuillBot

    Main page; Contents; Current events; Random article; About Wikipedia; Contact us; Pages for logged out editors learn more

  4. 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.

  5. Artificial intelligence content detection - Wikipedia

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

    Artificial intelligence detection software aims to determine whether some content (text, image, video or audio) was generated using artificial intelligence (AI).. However, the reliability of such software is a topic of debate, [1] and there are concerns about the potential misapplication of AI detection software by educators.

  6. Grammarly - Wikipedia

    en.wikipedia.org/wiki/Grammarly

    In September 2024, Grammarly announced the release of its Authorship tool, which attempts to identify the original source of a passage of text. It then designates the passage as written by the text's author, lifted from another source, or generated by AI. [24] [25] It's not clear to what extent such tools work. [26] [27]

  7. Content similarity detection - Wikipedia

    en.wikipedia.org/wiki/Content_similarity_detection

    Tokens – as with strings, but using a lexer to convert the program into tokens first. This discards whitespace, comments, and identifier names, making the system more robust against simple text replacements. Most academic plagiarism detection systems work at this level, using different algorithms to measure the similarity between token sequences.

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