An LLM-Associated Register Shift in Korean Journal Abstracts: A Morphology-Aware Excess-Vocabulary Study, 2018-2026
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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
- 2023GPT detectors are biased against non-native English writerscited by 175
- 2024Delving into LLM-assisted writing in biomedical publications through excess vocabularycited by 75
- 2024EXAONE 3.5: Series of Large Language Models for Real-world Use Casescited by 50
- 2025The Shrinking Landscape of Linguistic Diversity in the Age of Large Language Modelscited by 28
- 2024Empirical evidence of Large Language Model's influence on human spoken communicationcited by 27
- 2025Human-LLM Coevolution: Evidence from Academic Writingcited by 15
- 2024Why Does ChatGPT "Delve" So Much? Exploring the Sources of Lexical Overrepresentation in Large Language Modelscited by 8
- 2025How much are LLMs changing the language of academic papers after ChatGPT? A multi-database and full text analysiscited by 8
- 2025KatFishNet: Detecting LLM-Generated Korean Text through Linguistic Feature Analysiscited by 6
- 2025Examining Linguistic Shifts in Academic Writing Before and After the Launch of ChatGPT: A Study on Preprint Paperscited by 6
- 2025Estimating the prevalence of LLM-assisted text in scholarly writingcited by 4
- 2026Have LLM-associated terms increased in article full texts in all fields?cited by 2
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- 2026AI-Associated Lexical Shifts Across 34 Languages: Cross-Lingual Convergence and Diachronic Uptake in News Writingcited by 2
- 2026Most biomedical publications show signs of LLM-assisted writingcited by 2
- 2025Does Scientific Writing Converge to U.S. English? Evidence from Generative AI-Assisted Publicationscited by 1
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