An Empirical Study of AI Generated Text Detection Tools
Arslan Akram
Since ChatGPT has emerged as a major AIGC model, providing high-quality
responses across a wide range of applications (including software development
and maintenance), it has attracted much interest from many individuals. ChatGPT
has great promise, but there are serious problems that might arise from its
misuse, especially in the realms of education and public safety. Several AIGC
detectors are available, and they have all been tested on genuine text.
However, more study is needed to see how effective they are for multi-domain
ChatGPT material. This study aims to fill this need by creating a multi-domain
dataset for testing the state-of-the-art APIs and tools for detecting
artificially generated information used by universities and other research
institutions. A large dataset consisting of articles, abstracts, stories, news,
and product reviews was created for this study. The second step is to use the
newly created dataset to put six tools through their paces. Six different
artificial intelligence (AI) text identification systems, including "GPTkit,"
"GPTZero," "Originality," "Sapling," "Writer," and "Zylalab," have accuracy
rates between 55.29 and 97.0%. Although all the tools fared well in the
evaluations, originality was particularly effective across the board.