Scientific Writing in the Era of Large Language Models: A Computational Analysis of AI Versus Human-Created Content
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View abstract on PubMed
Summary
This summary is machine-generated.Large language models (LLMs) generate scientific text with distinct linguistic features, but these are not always identifiable by human experts. AI detection tools are crucial for maintaining scientific integrity.
Area Of Science
- Artificial Intelligence
- Scientific Communication
- Computational Linguistics
Background
- Large language models (LLMs) can accelerate scientific literature synthesis but risk spreading misinformation.
- Distinguishing AI-generated from human-authored scientific text is a growing challenge.
Purpose Of The Study
- To characterize linguistic features differentiating AI-generated and human-authored scientific text.
- To evaluate the performance of AI detection tools in this task.
Main Methods
- Computational synthesis of 34 cerebrovascular essays (12 AI-generated, 22 human-authored).
- Expert review by <i>Stroke</i> editorial board members.
- Comparison of expert performance with GPTZero AI detection tool.
- Linguistic feature extraction: syntax, semantics, readability, grade level, and perplexity.
Main Results
- AI-generated text exhibited lower word count, complexity, perplexity, and readability scores.
- Human experts misclassified 31.8% of human-written essays.
- GPTZero accurately classified 100% of AI-generated and 95.5% of human-written essays, but relied on limited text segments.
Conclusions
- LLMs produce scientific content with measurable linguistic differences from human writing.
- These differences are not consistently identifiable by human experts.
- Technology-assisted AI detection tools are essential for safeguarding scientific communication integrity.
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