This technology is a method for discerning artificial intelligence (AI)-generated text from human-written text using large language models.
With the growing abundance of artificial intelligence (AI)-generated text, there exists a need to distinguish between human and machine learning-assisted writing. Discerning machine-generated text from human-written text can be challenging because machine-learning algorithms inherently mimic the syntax and writing of a human being. Distinguishing between artificial and written text represents a technological challenge that impacts a plurality of fields, including journalism, creative writing, and academic writing.
This technology describes a method for identifying artificial intelligence (AI)-generated text by prompting a large language model to rewrite portions of a piece of text. In doing so, this technology exploits a common occurrence in writing, in which human-generated text is prone to more significant rewriting than AI-generated text when passed through a machine-learning algorithm. This technology utilizes a symbolic word output from large language models to reduce reliance on a deep neural network, boosting its reliability, generalizability, and adaptability. In addition, this technology remains robust even when the text generation is aware of the detection mechanism.
This technology has demonstrated improved detection for several established paragraph-level detection benchmarks, with F1 detection score gains up to 29 points.
Patent Pending
IR CU24139
Licensing Contact: Greg Maskel
