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An LLM-Associated Register Shift in Korean Journal Abstracts: A Morphology-Aware Excess-Vocabulary Study, 2018-2026

arXiv 2609.07447 ↗First posted 7 Sep 2026cs.CL cs.DLcited by 0

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Abstract (arXiv)

Excess vocabulary, a word's frequency above its pre-2023 trend, is how the change in scholarly English after 2022 has been measured. We adapt it to Korean with morphological units on 398,296 KCI abstracts (2018-August 2026), with 47,165 Vietnamese abstracts for comparison. Placebo floors are 0.1-2.2 points for the single-word statistic and at most 2.9 for the re-selected split-half set statistic. Korean abstracts show nothing in 2023, onset in late 2024, a rise through 2025 flattening in mid-2026: sisahada "suggest" appears in 21.4% of 2026 abstracts against 5.3% expected; plain verbs like araboda "look into" fall to a quarter of trend. Under stated assumptions the single-word conditional lower bound on LLM-processed abstracts is 3.5%, 10.5% and 16.1% for 2024-2026 and a split-half set bound 7.8%, 20.6% and 33.0%. Holzwarth et al.'s estimator under the same discipline gives 41.9% and 72.1% for 2025-2026. Subject-matter controls reduce but do not remove it: restricting the set to lemmas three language-model annotators all call style leaves 14.7 of the 33.0 points, and pairing each 2026 abstract with its journal's closest base-period abstract leaves 34.1. Tested translation routes do not explain it: the surface marks of translated Korean fall as the markers rise. In the same articles' English abstracts the excess appears a year earlier; where the English side carries none, the Korean shift persists at 30 to 66% of the rate where it does. Control abstracts from three providers reproduce the rising words, with marker turnover consistent with model generations; implied prevalences are scenario-dependent.

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References · 15

  1. 2023GPT detectors are biased against non-native English writerscited by 175
  2. 2024Delving into LLM-assisted writing in biomedical publications through excess vocabularycited by 75
  3. 2024EXAONE 3.5: Series of Large Language Models for Real-world Use Casescited by 50
  4. 2025The Shrinking Landscape of Linguistic Diversity in the Age of Large Language Modelscited by 28
  5. 2024Empirical evidence of Large Language Model's influence on human spoken communicationcited by 27
  6. 2025Human-LLM Coevolution: Evidence from Academic Writingcited by 15
  7. 2024Why Does ChatGPT "Delve" So Much? Exploring the Sources of Lexical Overrepresentation in Large Language Modelscited by 8
  8. 2025How much are LLMs changing the language of academic papers after ChatGPT? A multi-database and full text analysiscited by 8
  9. 2025KatFishNet: Detecting LLM-Generated Korean Text through Linguistic Feature Analysiscited by 6
  10. 2025Examining Linguistic Shifts in Academic Writing Before and After the Launch of ChatGPT: A Study on Preprint Paperscited by 6
  11. 2025Estimating the prevalence of LLM-assisted text in scholarly writingcited by 4
  12. 2026Have LLM-associated terms increased in article full texts in all fields?cited by 2
3 more
  1. 2026AI-Associated Lexical Shifts Across 34 Languages: Cross-Lingual Convergence and Diachronic Uptake in News Writingcited by 2
  2. 2026Most biomedical publications show signs of LLM-assisted writingcited by 2
  3. 2025Does Scientific Writing Converge to U.S. English? Evidence from Generative AI-Assisted Publicationscited by 1

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