University of Texas at Austin

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1. 5d
   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 Decoder · 🔥 0

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