AI vs. human: Study shows ChatGPT falls short in accuracy and authenticity of scientific abstracts LargeLanguageModels ScientificWriting Accuracy Integrity Abstracts EthicalGuidelines AIOutputDetectors FakeAbstracts npjDigitalMed
By Dr. Chinta SidharthanJun 27 2023Reviewed by Benedette Cuffari, M.Sc. In a recent study published in Npj Digital Medicine, researchers compare abstracts from studies published in high-impact medical journals with abstracts developed using the artificial intelligence large language model ChatGPT to evaluate the accuracy and reliability of using such large language models for scientific writing.
Large language models, of which ChatGPT is one of the largest, are based on neural network-based models trained on large-scale data to produce content that reads naturally. Generative Pretrained Transformer-3 , on which ChatGPT is built, is trained using 175 billion parameters and can produce coherent and fluent content that is often difficult to distinguish from content created by humans.
For the test group, ChatGPT was used to generate 50 abstracts based on selected journals and titles from the list. To this end, the researchers asked ChatGPT to create an abstract for a study with a given title in the style of a given journal. ChatGPT abstracts lack authenticity The abstracts generated through ChatGPT showed a high probability of being AI-generated, with a median score of 99.89%. Comparatively, original abstracts had a median score of 0.02%, thus indicating that these abstracts had a low probability of being generated using an AI language tool.
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