AI polishing shrinks the range of writing style by 21 to 50 percent: Nature Human Behaviour analyzes 880,000 texts

Using large language models as writing assistants preserves core content while reducing writing-complexity variance by a statistically significant 21 to 50 percent across datasets and models (P ≤ 0.05), according to a study published in Nature Human Behaviour on August 24, 2026. "The shrinking landscape of linguistic diversity in the age of large language models," written by researchers at the University of Southern California, runs three studies across seven datasets from different domains and more than 880,000 texts. The conclusion is that LLMs homogenize writing styles while amplifying patterns associated with dominant characteristics and suppressing others, emphasizing conformity over individuality. ASAP summarizes the Nature Human Behaviour article page as the primary source.

Seven datasets and a three-part design

The seven datasets range from psychology essays to floor speeches in the US Congress. The published data availability statement lists Reddit r/WritingPrompts posts, arXiv abstracts, Patch.com local news articles, US Congressional Record floor speeches, YourMorals Facebook posts paired with Moral Foundations Questionnaire responses, the Empathic Conversations dataset, and the Essays corpus maintained by James W. Pennebaker. Six of the seven are publicly available and only the Essays corpus is access restricted.

One strand of the design has models rewrite original texts, then compares the two versions. The models used are GPT-3.5, Gemini and Llama 3, and the rewriting instructions span 12 prompts: syntax and grammar, rephrase, clarity, conciseness, fluency, naturalness, plain language, polish, academic, formalize, smooth, and improve readability. Sample sizes for this experiment are 2,348 for Essays, 3,641 for YourMorals, 711 for Empathic Conversations, 710 for Congress, and 1,000 each for arXiv, Reddit and Patch News.

A second strand tracks real-world corpora over time. It aggregates 80,238 arXiv papers, 379,583 Patch News articles and 318,490 Reddit stories by month to observe change around the public release of ChatGPT in November 2022. The third strand, which the paper calls Study 3, trains classifiers on original texts and reapplies them to the rewritten versions to measure how far predictions about the author break down.

Meaning survives, texture does not

The finding in the abstract is split across two layers that move apart, because core content is retained when LLMs polish and rewrite texts while the same operation homogenizes writing styles. Writing-complexity variance falls by 21 to 50 percent under that treatment. That reduction holds across datasets and models and is significant at P ≤ 0.05.

Content retention was checked with embeddings. Each original and its rewritten counterpart were embedded with OpenAI text-embedding-ada-002 and compared by cosine similarity per text, with each original serving as its own paired baseline for its rewrites. That pairing is why no separate control group is appropriate.

Five features carry the style side of the analysis: the Vocabulary Simpson Index, Vocabulary Shannon Entropy, Average Dependency Link Length, Type-Token Ratio, and Hapax Legomena, meaning words that appear exactly once. They measure how evenly vocabulary is distributed, how far syntactic dependencies reach, and how much once-only vocabulary a writer uses, which is the layer where individual writing habits show most clearly.

What happened in real corpora after ChatGPT

The time-series strand is the part that leaves the lab, covering 80,238 arXiv papers, 379,583 Patch News articles and 318,490 Reddit stories aggregated by month around the November 2022 release of ChatGPT. Across all three corpora, the monthly variance of all five complexity features shrinks after ChatGPT's release, alongside an increase in the AI-attribution rate over the same window. The monthly means of those same features move the other way and increase.

That combination is the most instructive observation in the paper. A rising mean says writing became more elaborate and more lexically rich on average, and a falling variance says the elaboration converged on similar forms. The quality indicator for individual texts and the diversity indicator for the population moved in opposite directions.

How the signal that reads people breaks down

Study 3 is the strand that measures directly how much person-level information survives rewriting, and it reports mean F1 for classifiers trained on original texts and reapplied to their GPT-3.5, Gemini and Llama 3 rewrites. The breakdown runs by LLM, by rewriting prompt, by featurization technique, and by classifier family. The trait groups carry sample sizes of 805 for age group, 2,348 for personality, 711 for empathy, 710 for gender, 3,641 for morality and 710 for affiliation.

The direction of failure was tracked separately. Taking only texts that had been classified correctly in their original form as the denominator, the analysis reports the percentage whose post-rewrite prediction shifted in each direction, split by LLM and by the syntax-grammar and rephrase prompt families. Imbalance across sub-dimensions is handled through Delta, a proxy for imbalances between predicted class frequencies on original versus rewritten texts, tested with a two-sided paired Wilcoxon signed-rank test and reported with rank-biserial correlation r and 95 percent bootstrap confidence intervals from 2,000 resamples.

Why a rising mean with a falling variance matters

Summarizing the Nature Human Behaviour result as "AI makes writing worse" is the wrong reading, because the paper does not show individual texts getting worse. What it shows is the ways they get better collapsing into one. For any single author, rewriting is a win. Sentences read more smoothly, vocabulary widens, grammar gets tidied. The loss never lands on the author's own ledger, it lands on the collective one.

That structure has a familiar shape, where individually rational choices produce a shared loss. Nobody set out to reduce diversity and every user only tried to improve their own draft, yet the information society used to read out of language gets thinner. This is why user education or personal restraint is a weak lever. Reversing it requires the tools to change, and specifically requires asking why "better writing" has exactly one default.

That leaves one concrete question for tool designers. Today's rewrite features generally return a single result, and that result converges toward the majority style of the training data. Constraining a rewrite to preserve the user's own style statistics, offering several candidates that diverge in different directions, or simply surfacing how far a lexical diversity metric dropped are not hard engineering problems. The most actionable item this paper leaves behind sits there.

Hiring, diagnosis and personalization break first

The four affected areas named in Nature Human Behaviour are diagnostic processes, personalization efforts, hiring assessments and cultural preservation. The list sounds abstract, yet each one is already a working practice that reads people out of their language.

Hiring is the most direct case. Inferring a candidate's disposition from cover letters and application essays is a long-standing practice, and parts of that reading are now automated. Once most applicants polish their writing with models from the same family, the signal being read gets thinner. This paper's experimental structure is precisely that situation, because applying a classifier trained on originals to rewritten text is the same operation as applying a baseline built from human-written applications to AI-polished ones. Any organization running automated screening should check what vintage of data its baseline was built on.

Clinical and psychological uses have a different shape. Research on detecting early signals of depression or cognitive decline from language markers is deep, and those markers largely sit in the same layer of lexical diversity and syntactic complexity that this paper reports as homogenized. For any tool relying on patient-authored text, whether an AI assistant touched the drafting changes the measurement. Personalization and marketing face the mirror image of the problem: if patterns associated with dominant characteristics are amplified, personalization systems will increasingly misclassify users with minority traits toward the majority.

The study contains no Korean

The first limit for practitioners outside the English-speaking world is language coverage. All seven datasets in the availability statement are English corpora, and most sources are US-centric, including Reddit, arXiv, Patch.com and the Congressional Record. Nothing inside this paper licenses transferring its conclusions directly to Korean writing.

Transferring the method is still worth doing. The code is public under the MIT license on Zenodo (DOI 10.5281/zenodo.21633457) and includes scripts for preprocessing, LLM rewriting, classifier training, statistical analysis and figure generation, requiring nothing beyond Python 3.11.7 and R 4.4.2. Metrics such as the Vocabulary Simpson Index and Type-Token Ratio are language neutral, while a syntax-bound metric like Average Dependency Link Length needs a Korean parser swapped in. Korean also inflates Type-Token Ratio through particles and endings, which needs separate handling.

The hypothesis might land harder in Korean. Korean training data is smaller in absolute volume than English, and the stylistic range of instruction-tuning data is narrower, so the target style a model converges on plausibly spans a narrower band. That, however, is a question waiting to be tested rather than something this paper tested. Running the public code over Korean corpora is a high-return follow-up for a linguistics or psychology lab.

What to read carefully

The strength of the causal claim needs to be sized correctly. The rewriting experiment is a controlled paired comparison between an original and its rewrite, which supports causal reading, while the time-series analysis observes changes in the open web and cannot establish that the ChatGPT release was the only driver. Platform policies, search ranking and the composition of who writes online all moved during the same period.

The AI-attribution rate is itself an estimate. That rising series comes from detector judgments, and the reliability of machine-generated text detectors is a known dispute that this paper cites several papers about. The "AI use went up" axis therefore rests on estimation rather than observation.

Model generation deserves a note. The experiments used GPT-3.5, Gemini and Llama 3, all earlier than the models in production use as of August 2026, and this paper cannot say whether newer models preserve style better or worse. Sample sizes also differ sharply by strand: the time-series work spans tens to hundreds of thousands of texts per corpus, while the rewriting experiment ranges from 711 to 3,641 per dataset, with the Congress and gender items smallest at 710.

Finally, access. The article is subscription content in Nature Human Behaviour, and what anyone can read is the abstract, the extended data figure captions, the data and code availability statements, and the reference list. Every figure in this summary was verified inside that open scope, while the main text tables and coefficients require a subscription or individual purchase. The funding is worth reading alongside the results: the work was supported in part by the Army Research Laboratory under contract W911NF-23-2-0183, by DARPA INCAS HR001121C0165, and by the Air Force Office of Scientific Research A9550-23-1-0463, and the paper states the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Source: Zhivar Sourati, Farzan Karimi-Malekabadi, Meltem Ozcan and colleagues, "The shrinking landscape of linguistic diversity in the age of large language models" (Nature Human Behaviour, August 24, 2026), summarized by ASAP.