ChatGPT is an AI language model that can generate diverse and fluent text across a wide range of topics. However, it is known to have factual errors and can sometimes create inaccurate information. This has raised concerns about the authenticity and originality of the text it produces. Some educational institutions have even restricted its usage due to the ease of creating content.
LLMs like ChatGPT do not copy responses verbatim but instead use patterns and information from their training data to generate new content. While they strive for originality and accuracy, they are not infallible. Therefore, it is important for users to exercise discretion and not rely solely on AI-generated content for important decisions or expert advice.
Several detection frameworks, like DetectGPT and GPTZero, have been developed to identify content generated by LLMs. One such framework is Ghostbuster, developed by researchers from the University of California. Ghostbuster uses a structured search and linear classification method to detect AI-generated content based on its training process and other features.
The performance of Ghostbuster has proven to surpass other models, achieving an F1 score of 97.0 and outperforming similar frameworks. It has demonstrated a robust ability to detect AI-generated content across various datasets and domains.
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