# Homogenization of professional and scientific prose > Source-anchored research for Homogenization of professional and scientific prose. Run status: COMPLETED. Parent: [Human adoption of LLM vocabulary and its effect on tic baselines](https://llms-explorer.com/tree/ai-isms-human-convergence/) · 3 facets · 7 facts · page: https://llms-explorer.com/tree/homogenization-of-professional-and-scientific-prose/ ## 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](https://pmc.ncbi.nlm.nih.gov/articles/PMC12219543/) - 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](https://arxiv.org/html/2508.01491v1) - 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](https://arxiv.org/abs/2603.18161) ## 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](https://pmc.ncbi.nlm.nih.gov/articles/PMC12219543/) ## 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](https://pmc.ncbi.nlm.nih.gov/articles/PMC12219543/) - 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](https://arxiv.org/html/2508.01491v1) - 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](https://arxiv.org/html/2508.01491v1)