Homogenization of professional and scientific prose
Parent: Human adoption of LLM vocabulary and its effect on tic baselines · Published reference · snapshot 2026-09-23
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Source-anchored research for Homogenization of professional and scientific prose. Run status: COMPLETED.
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Measurements and reference values
- LLM-marker usage varies widely: about 20% in computational fields and in China/South Korea/Taiwan, about 5% in English-speaking countries; MDPI (21%) and Frontiers (20%) exceed Nature/Science (~7-10%). Measured rates are likely lower bounds. (low confidence) (verified as of 2025-07-01) [source]
- Sourati et al. (USC) report declining writing-complexity variance on Reddit, news and academic preprints, and that LLM polishing/rewriting reduces that variance (reported 21-50% across datasets/models); the review cites this as evidence of stylistic homogenization in scientific writing. (medium confidence) [source]
- Even when prompted for grammar-only edits, LLMs substantially alter meaning; in a user study heavy LLM use raised neutral-stance essays by nearly 70%, and AI-generated peer reviews scored about one point higher than expected and devalued clarity and significance. (low confidence) [source]
Problems, failure modes and limitations
- The excess-word method cannot flag individual abstracts, cannot separate direct LLM use from stylistic borrowing, and cannot fully separate LLM effects from other emerging linguistic trends. (low confidence) [source]
Comparisons and alternatives
- Excess-vocabulary analysis found 454 excess style words in 2024 biomedical abstracts, versus 190 at the 2021 COVID peak; the authors estimate at least 13.5% of 2024 abstracts were LLM-processed. (low confidence) (verified as of 2025-07-01) [source]
- Cited evidence that people adopt LLM framing (Jakesch et al. 2023) and that AI-assisted ideation reduces semantic diversity (Anderson et al. 2024) supports the mechanism, but the review notes longitudinal effects and correlation-versus-causation gaps remain open. (low confidence) [source]
- Rewriting experiments apply one LLM uniformly to all abstracts, so they give an upper bound on homogenization rather than real-world practice; effects are stronger for Reddit and academic writing than for news. (low confidence) [source]
Children
- No children recorded.