To know how our AI content detector works, it helps to understand how AI content is created.
To create AI content, software must first be trained with a series of language models. The software is fed thousands of books, articles, and other written copies. The software consumes this content and learns the relationships between words and phrases, taking note of the statistical patterns of one word following the next, in addition to many other complex signals. Once a language model, like GPT, has been fully trained, it is able to read and understand questions posed to it, and respond with unique responses that are a combination of the thousands of articles it consumed during training.
Detecting AI content works in a similar manner. Generative AI content detectors analyze the content it is provided, looking for language nuances, statistical irregularities, and issues in authenticity and uniqueness. The tool then assigns a score based on how likely one word, phrase, etc. is to follow the next. Content that is considered ‘irregular’ is considered more unique, and thus, more likely to have been written by a human, than a chatbot or other AI tool.
The process of analyzing complex text to identify AI-generated content is similar to the natural language processing analysis done to detect plagiarism, which is why Quetext’s AI detector outperforms other detectors.