Literary Non-Style in LLM-Generated Text
Prior work on LLM-generated text has demonstrated quantitative and qualitative departures from text produced by humans. LLM-generated texts differ from human writing in style, resulting in a characteristic textual "feel," while the semantic range of LLMs is much restricted compared to that of humans. In this contribution, I note simple but consistent patterns in the statistical distribution of n-grams within LLM-generated text. Via qualitative analysis of these n-grams, I reveal deficiencies in LLM style. Because higher-order n-grams correlate to semantic content, I conclude that questions of
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- PossiblePossibly related (embedding) · 52%Are all LLM research papers nowadays 100+ pages beasts?[D] →
- PossiblePossibly related (embedding) · 47%The Large Language Model (LLM) understands the world through human texts. A world model is needed f.. - 매일경제 →
- PossiblePossibly related (embedding) · 46%Improving machine-translated novels via style transfer — looking for advice on the faithfulness/fluency tradeoff [P] →
- PossiblePossibly related (embedding) · 45%Detecting LLM-Generated Texts with "Classical" Machine Learning →
- LinkedLinked via arxiv author · 85%Cory Massaro →
“Literary Non-Style in LLM-Generated Text”