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Thomas Hikaru Clark submitted the paper "Why is AI-generated language so hard to understand?", investigating why AI-generated text is harder to understand than human writing. The paper points out that, compared with human writing, 9 frontier and near-frontier models more frequently use inanimate subjects paired with animate verbs and low-frequency n-grams, and most models also use longer dependencies, more noun stacking, and reduced object relative clauses, with clear differences among model families. The author proposes that human language is optimized for successful communication, whereas AI text is increasingly optimized for information-density objectives such as mathematics, code, and reasoning; fine-tuning language models under a length penalty can reproduce behavior that deviates from human language, providing a proof of concept for this hypothesis. Subsequent reports stated that the study tested 14 models in total—Kimi K2.5, GLM 5, Claude Sonnet 5/Opus 5/Opus 5.5/Fable 5, GPT-5.6 Luna/Terra/Sol, Olmo, and Llama—and concluded that the problem affects all frontier and near-frontier models as well as some open-weight models.
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Oct 27
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Oct 26
Unite.AI
Research: Why AI-generated text is harder to understand than you think
A new study points out that frontier models such as ChatGPT, Claude, and Gemini, because their training rewards efficient problem-solving more than clear communication, tend to output highly compressed language that requires extra decoding, involving structures such as anthropomorphizing inanimate subjects, stacked noun compounds, and noun-to-verb conversion. The study tested 14 models in total—Kimi K2.5, GLM 5, Claude Sonnet 5/Opus 5/Opus 5.5/Fable 5, GPT-5.6 Luna/Terra/Sol, Olmo, and Llama—and concluded that the problem affects all frontier and near-frontier models as well as some open-weight models.
Oct 25
Hacker News popular
arXiv paper: Why is AI-generated language hard to understand?
arXiv paper 2610.29162 studies why AI-generated text is harder to understand: compared with human writing, all 9 frontier and near-frontier models more frequently use inanimate subjects paired with animate verbs and low-frequency n-grams, and most models also use longer dependencies, more noun stacking, and reduced object relative clauses, with clear differences among model families. The authors propose that human language is optimized for successful communication, whereas AI text is increasingly optimized for information-density objectives such as math, code, and reasoning; fine-tuning language models under a length penalty can reproduce behavior that deviates from human language, providing a proof of concept for this hypothesis.
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