The greater the amount of unpredictable text, the more likely the text is from a human. Therefore, the results are presented as a color-coded histogram, ranking AI-generated text versus human-generated text. GLTR basically uses bots to identify text written by bots, since bots are less likely to select unpredictable words.

Like GPTZero, it doesn’t present its final results as a clear “human” or “bot” distinction. Just for fun, I included Giant Language Model Test Room (GLTR), developed by researchers from the MIT-IBM Watson AI Lab and Harvard Natural Language Processing Group. The tool assigns a number to both metrics - the lower the number, the greater possibility that the text was created by a bot. Burstiness measures overall randomness for all sentences in a text, while perplexity measures randomness in a sentence. GPTZero breaks the metrics into perplexity and burstiness. It’s differs from the rest in how the “algiarism” (AI-assisted plagiarism) model presents its results. GPTZero is a home-brewed beta tool hosted on Streamlit and created by Princeton University student Edward Zen.

The results are given a percentage score of how likely it is that the content is human-generated. You can either add a URL to scan the content (writer only) or manually add text. Two more tools that have clean UIs are Writer AI Content Detector and Content at Scale. Output Detector is easy to use - users just have to enter text into a text field and the tool will immediately provide its assessment of how likely it is that the text came from a human or not. GPT-2 Output Detector comes straight from ChatGPT developer OpenAI to demonstrate that it has a bot capable of detecting chatbot text.

Gpt chatbot