Study identifies six linguistic patterns that make AI text harder to read
Researchers from New York University and the University of Texas at Austin analyzed text from 14 language models, including 120 conversations, 45 coding sessions and 980 essays, and compared it with human writing. They identified six recurring constructions that the models overused: unusual word combinations, widely separated related sentence elements, shortened references with dropped connectors, stacked nouns, nouns converted into verbs, and animate verbs applied to inanimate subjects. Llama and the Olmo base model were closest to human text. In a proof-of-concept experiment, a smaller model trained with a penalty for longer answers favored shorter phrasing under certain conditions.
The study provides comparative evidence that some model outputs systematically use constructions that can make them harder for people to read. The researchers also argue that training priorities may reward correctness and information density over clarity, although the report presents post-training and targeted fine-tuning as possible explanations rather than independently established causes.
Researchers find six patterns that make AI text so exhausting to read
Matthias Bastian View the LinkedIn Profile of Matthias Bastian
Oct 26, 2026
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A study from New York University and the University of Texas at Austin shows that frontier language models systematically overuse linguistic patterns that make their output harder to read.
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