Abstracts Written By ChatGPT Fool Scientists

An artificial intelligence (AI) chatbot can write such convincing fake research paper abstracts that scientists are often unable to spot them, according to a preprint posted on the bioRxiv server in late December. Researchers are divided over the implications for science, Holly Else reported for Nature.

Photo Insert: The chatbot, ChatGPT, creates realistic and intelligent-sounding text in response to user prompts.

“I am very worried,” says Sandra Wachter, who studies technology and regulation at the University of Oxford, UK, and was not involved in the research. “If we’re now in a situation where the experts are not able to determine what’s true or not, we lose the middleman that we desperately need to guide us through complicated topics,” she adds.

Earlier this month, the Fortieth International Conference on Machine Learning, a large AI conference that will be held in Honolulu, Hawaii, in July, announced that it has banned papers written by ChatGPT and other AI language tools.

The chatbot, ChatGPT, creates realistic and intelligent-sounding text in response to user prompts.

It is a “large language model,” a system based on neural networks that learn to perform a task by digesting huge amounts of existing human-generated text. Software company OpenAI, based in San Francisco, California, released the tool on Nov. 30, 2022, and it is free to use.

Researchers have been grappling with the ethical issues surrounding its use because much of its output can be difficult to distinguish from human-written text. Scientists have published a preprint and an editorial written by ChatGPT.

A group led by Catherine Gao at Northwestern University in Chicago, Illinois, has used ChatGPT to generate artificial research paper abstracts to test whether scientists can spot them.

The ChatGPT-generated abstracts sailed through the plagiarism checker: the median originality score was 100%, which indicates that no plagiarism was detected.

Human reviewers didn't do much better: they correctly identified only 68% of the generated abstracts and 86% of the genuine abstracts. They incorrectly identified 32% of the generated abstracts as being real and 14% of the genuine abstracts as being generated.

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