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

arXiv: https://arxiv.org/abs/2609.07447 · HTML: https://mlsanity.com/paper/2609.07447/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 23 references that are in the map.
- First posted on arXiv 7 Sep 2026.

## Builds on (most cited first, 15 of 15)

- [GPT detectors are biased against non-native English writers](https://mlsanity.com/paper/2304.02819/) (2023)
- [Delving into LLM-assisted writing in biomedical publications through excess vocabulary](https://mlsanity.com/paper/2406.07016/) (2024)
- [EXAONE 3.5: Series of Large Language Models for Real-world Use Cases](https://mlsanity.com/paper/2412.04862/) (2024)
- [The Shrinking Landscape of Linguistic Diversity in the Age of Large Language Models](https://mlsanity.com/paper/2502.11266/) (2025)
- [Empirical evidence of Large Language Model's influence on human spoken communication](https://mlsanity.com/paper/2409.01754/) (2024)
- [Human-LLM Coevolution: Evidence from Academic Writing](https://mlsanity.com/paper/2502.09606/) (2025)
- [Why Does ChatGPT "Delve" So Much? Exploring the Sources of Lexical Overrepresentation in Large Language Models](https://mlsanity.com/paper/2412.11385/) (2024)
- [How much are LLMs changing the language of academic papers after ChatGPT? A multi-database and full text analysis](https://mlsanity.com/paper/2509.09596/) (2025)
- [KatFishNet: Detecting LLM-Generated Korean Text through Linguistic Feature Analysis](https://mlsanity.com/paper/2503.00032/) (2025)
- [Examining Linguistic Shifts in Academic Writing Before and After the Launch of ChatGPT: A Study on Preprint Papers](https://mlsanity.com/paper/2505.12218/) (2025)
- [Estimating the prevalence of LLM-assisted text in scholarly writing](https://mlsanity.com/paper/2512.01560/) (2025)
- [Have LLM-associated terms increased in article full texts in all fields?](https://mlsanity.com/paper/2604.07565/) (2026)
- [AI-Associated Lexical Shifts Across 34 Languages: Cross-Lingual Convergence and Diachronic Uptake in News Writing](https://mlsanity.com/paper/2605.25358/) (2026)
- [Most biomedical publications show signs of LLM-assisted writing](https://mlsanity.com/paper/2608.10715/) (2026)
- [Does Scientific Writing Converge to U.S. English? Evidence from Generative AI-Assisted Publications](https://mlsanity.com/paper/2511.11687/) (2025)

## 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.