Using AI to write scholarly publications

January 25, 2023Accountability in ResearchOpen Access

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Key Points

To examine the emergence of natural language processing systems in academic writing and evaluate the ethical implications of using AI to generate scholarly content.
Conceptual review of modern artificial intelligence and natural language processing architectures, including generative pre-trained transformer models.
Analysis of technical mechanisms underlying language generation, specifically tokenization, deep neural networks, and supervised machine-learning algorithms trained on multi-terabyte datasets.
Modern natural language processing systems have transitioned from rule-based editing tools to generative statistical models parameterized by billions of weights capable of producing scholarly text.
Generative models reduce erroneous outputs through iterative supervised training but inherently lack semantic comprehension and the ability to verify factual truth, presenting risks to scientific integrity.

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Review explores the emergence of generative AI in scholarly publishing, highlighting critical concerns for research ethics and scientific integrity.

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Hosseini et al. (2023) studied this question.

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