This New Tool From OpenAI Can Detect AI-Written Text: Here Is How
This New Tool From OpenAI Can Detect AI-Written Text: Here Is How
The AI Text Classifier is a fine-tuned GPT model that predicts whether a piece of text was generated by AI from a variety of sources, such as ChatGPT.

After launching the most popular AI chatbot — ChatGPT, the Microsoft-backed AI research and deployment company OpenAI has now launched a new tool called ‘The AI Text Classifier; which can detect AI-generated content.

The AI Text Classifier is a fine-tuned GPT model that predicts whether a piece of text was generated by AI from a variety of sources, such as ChatGPT.  “This classifier is available as a tool to spark discussions on AI literacy,” the company said on the new tool’s page.

According to the company, AI Text Classifier’s reliability typically improves as the length of the input text increases. Compared to a previously released classifier, this new tool is significantly more reliable on text from more recent AI systems.

Also, the classifier has a number of important limitations. It should not be used as a primary decision-making tool, but instead as a complement to other methods of determining the source of a piece of text. The classifier is very unreliable on short texts (below 1,000 characters). Even longer texts are sometimes incorrectly labeled by the classifier.

Sometimes human-written text will be incorrectly but confidently labeled as AI-written by our classifier. “We recommend using the classifier only for English text. It performs significantly worse in other languages and it is unreliable on code,” the website reads.

Text that is very predictable cannot be reliably identified. For example, it is impossible to predict whether a list of the first 1,000 prime numbers was written by AI or humans, because the correct answer is always the same. AI-written text can be edited to evade the classifier.

“Classifiers like ours can be updated and retrained based on successful attacks, but it is unclear whether detection has an advantage in the long-term. Classifiers based on neural networks are known to be poorly calibrated outside of their training data. For inputs that are very different from text in our training set, the classifier is sometimes extremely confident in a wrong prediction,” the company said.

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