Evidence map›Paper›PMID 42637911›Full record

ArticleNature human behaviour2026

The shrinking landscape of linguistic diversity in the age of large language models.

Zhivar Sourati, Farzan Karimi-Malekabadi, Meltem Ozcan, Colin McDaniel, Alireza Ziabari, Jackson Trager, Ala N Tak, Meng Chen, Fred Morstatter, Morteza Dehghani

Abstract read
PubMed Publisher
In one paragraph

Article in Nature human behaviour, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Artificial Intelligence and the Psychology of Human Connection.Perspectives on psychological science : a journal of the Association for Psychological Science · 2026
    Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Zhivar SouratiDepartment of Computer Science, University of Southern California, Los Angeles, CA, USA. souratih@usc.edu.ORCID http://orcid.org/0000-0003-2129-6165
Farzan Karimi-Malekabadi *Center for Computational Language Sciences, University of Southern California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0002-3381-7071
Meltem Ozcan *Department of Psychology, University of Southern California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0002-5054-8264
Colin McDanielDepartment of Psychology, University of Southern California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0003-3880-164X
Alireza ZiabariDepartment of Computer Science, University of Southern California, Los Angeles, CA, USA.
Jackson TragerCenter for Computational Language Sciences, University of Southern California, Los Angeles, CA, USA.
Ala N TakDepartment of Computer Science, University of Southern California, Los Angeles, CA, USA.
Meng ChenDepartment of Psychology, University of Southern California, Los Angeles, CA, USA.
Fred MorstatterDepartment of Computer Science, University of Southern California, Los Angeles, CA, USA.
Morteza DehghaniDepartment of Computer Science, University of Southern California, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0002-9478-4365

Funding

United States Department of Defense | Defense Advanced Research Projects Agency (DARPA) HR001121C0165United States Department of Defense | United States Air Force | AFMC | Air Force Office of Scientific Research (AF Office of Scientific Research) A9550-23-1-0463United States Department of Defense | United States Army | U.S. Army Research, Development and Engineering Command | Army Research Laboratory (U.S. Army Research Laboratory) W911NF-23-2-0183
6 · The paper itself

Abstract

Language is far more than a communication tool; it encodes a wealth of information about a person's identity, psychological state and social context, providing valuable insights for diverse fields including psychology, marketing and healthcare. Across three studies spanning seven datasets in different domains and over 880,000 texts, we show that the widespread adoption of large language models (LLMs) as writing assistants is linked to declines in linguistic diversity, interfering with the societal and psychological insights language provides. While core content is retained when LLMs polish and rewrite texts, LLMs also homogenize writing styles, reducing writing-complexity variance by a statistically significant 21-50% across datasets and models (P ≤ 0.05), and amplify patterns associated with dominant characteristics while suppressing others, emphasizing conformity over individuality. These trends hold across different LLMs, prompts and contexts, with potential implications for diagnostic processes, personalization efforts, hiring assessments and cultural preservation.

Identifiers

PMID42637911

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.