Psycholinguistics
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Why is AI generated language so hard to understand?
research
2 min read
A study submitted to arXiv on October 21, 2026 finds that AI-generated text uses several linguistic features associated with greater human processing difficulty more often than comparison texts. The researchers analyzed a new corpus of AI writing from diverse domains and measured features including object relative clauses, long syntactic dependencies, and low-frequency word sequences. AI models used difficult features more often than paraphrases produced through round-trip translation. Against human writing, all nine frontier and near-frontier models examined used more inanimate subjects paired with animate verbs and more low-frequency n-grams. Most models also produced longer dependencies, more stacked nouns, and more reduced object relatives in conversational text, although the results varied substantially among model families. The researchers propose that human language is shaped for successful communication, while AI output is increasingly shaped by objectives such as mathematics, code, and reasoning that reward information density. As a proof of concept, fine-tuning a language model with a length penalty caused its language behavior to diverge from human-like patterns. The findings identify potential mismatches between the linguistic demands of human readers and language-model output and suggest that model training objectives may contribute to harder-to-understand text.