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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.
Citation patterns represent subsequences non-exclusively containing citations shared by the documents compared. [27] [29] Factors, including the absolute number or relative fraction of shared citations in the pattern, as well as the probability that citations co-occur in a document are also considered to quantify the patterns' degree of similarity.
A string-searching algorithm, sometimes called string-matching algorithm, is an algorithm that searches a body of text for portions that match by pattern. A basic example of string searching is when the pattern and the searched text are arrays of elements of an alphabet ( finite set ) Σ.
The similarity of two strings and is determined by this formula: twice the number of matching characters divided by the total number of characters of both strings. The matching characters are defined as some longest common substring [3] plus recursively the number of matching characters in the non-matching regions on both sides of the longest common substring: [2] [4]
In computer science, an algorithm for matching wildcards (also known as globbing) is useful in comparing text strings that may contain wildcard syntax. [1] Common uses of these algorithms include command-line interfaces, e.g. the Bourne shell [2] or Microsoft Windows command-line [3] or text editor or file manager, as well as the interfaces for some search engines [4] and databases. [5]
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ChatGPT Search (originally SearchGPT) is a search engine developed by OpenAI. It combines traditional search engine features with generative pretrained transformers (GPT) to generate responses, including citations to external websites.
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.