# The Shrinking Landscape of Linguistic Diversity in the Age of Large Language Models

arXiv: https://arxiv.org/abs/2502.11266 · HTML: https://mlsanity.com/paper/2502.11266/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 80 references that are in the map.
- First posted on arXiv 16 Feb 2025.

## Builds on (most cited first, 20 of 20)

- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [State of What Art? A Call for Multi-Prompt LLM Evaluation](https://mlsanity.com/paper/2401.00595/) (2023)
- [SentEval: An Evaluation Toolkit for Universal Sentence Representations](https://mlsanity.com/paper/1803.05449/) (2018)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes](https://mlsanity.com/paper/1711.08412/) (2017)
- [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013)
- [Generative artificial intelligence enhances creativity but reduces the diversity of novel content](https://mlsanity.com/paper/2312.00506/) (2023)
- [RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors](https://mlsanity.com/paper/2405.07940/) (2024)
- [Homogenization Effects of Large Language Models on Human Creative Ideation](https://mlsanity.com/paper/2402.01536/) (2024)
- [The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic Text](https://mlsanity.com/paper/2311.09807/) (2023)
- [Ghostbuster: Detecting Text Ghostwritten by Large Language Models](https://mlsanity.com/paper/2305.15047/) (2023)
- [The Political Preferences of LLMs](https://mlsanity.com/paper/2402.01789/) (2024)
- [Delving into LLM-assisted writing in biomedical publications through excess vocabulary](https://mlsanity.com/paper/2406.07016/) (2024)
- [Unintended Impacts of LLM Alignment on Global Representation](https://mlsanity.com/paper/2402.15018/) (2024)
- [Contrasting Linguistic Patterns in Human and LLM-Generated News Text](https://mlsanity.com/paper/2308.09067/) (2023)
- [AI Suggestions Homogenize Writing Toward Western Styles and Diminish Cultural Nuances](https://mlsanity.com/paper/2409.11360/) (2024)
- [The Widespread Adoption of Large Language Model-Assisted Writing Across Society](https://mlsanity.com/paper/2502.09747/) (2025)
- [Echoes in AI: Quantifying lack of plot diversity in LLM outputs](https://mlsanity.com/paper/2501.00273/) (2024)
- [Open-Source LLMs for Text Annotation: A Practical Guide for Model Setting and Fine-Tuning](https://mlsanity.com/paper/2307.02179/) (2023)
- [A Practical Examination of AI-Generated Text Detectors for Large Language Models](https://mlsanity.com/paper/2412.05139/) (2024)

## Built on by (most cited first, 29 of 29)

- [Cultivating Pluralism In Algorithmic Monoculture: The Community Alignment Dataset](https://mlsanity.com/paper/2507.09650/) (2025)
- [The Homogenizing Effect of Large Language Models on Human Expression and Thought](https://mlsanity.com/paper/2508.01491/) (2025)
- [Poor Alignment and Steerability of Large Language Models: Evidence from College Admission Essays](https://mlsanity.com/paper/2503.20062/) (2025)
- [Linguistic and Embedding-Based Profiling of Texts generated by Humans and Large Language Models](https://mlsanity.com/paper/2507.13614/) (2025)
- [The Basic B*** Effect: The Use of LLM-based Agents Reduces the Distinctiveness and Diversity of People's Choices](https://mlsanity.com/paper/2509.02910/) (2025)
- [How important is language for human-like intelligence?](https://mlsanity.com/paper/2509.15560/) (2025)
- [Generative AI in Sociological Research: State of the Discipline](https://mlsanity.com/paper/2511.16884/) (2025)
- [The Homogenization Problem in LLMs: Towards Meaningful Diversity in AI Safety](https://mlsanity.com/paper/2601.06116/) (2026)
- [EPSVec: Efficient and Private Synthetic Data Generation via Dataset Vectors](https://mlsanity.com/paper/2602.21218/) (2026)
- [Sampling More, Getting Less: Calibration is the Diversity Bottleneck in LLMs](https://mlsanity.com/paper/2605.11128/) (2026)
- [The Variance Paradox: How AI Reduces Diversity but Increases Novelty](https://mlsanity.com/paper/2508.19264/) (2025)
- [Group Selection as a Safeguard Against AI Substitution](https://mlsanity.com/paper/2602.03541/) (2026)
- [Mixture of Masters: Sparse Chess Language Models with Player Routing](https://mlsanity.com/paper/2602.04447/) (2026)
- [Personalized Group Relative Policy Optimization for Heterogenous Preference Alignment](https://mlsanity.com/paper/2603.10009/) (2026)
- [How AI Writing Assistance Distorts Perceptions of AI Users and Their Opinions](https://mlsanity.com/paper/2604.22503/) (2026)
- [AI Coding Agents in Social Science: Methodologically Diverse, Empirically Consistent, Interpretively Vulnerable](https://mlsanity.com/paper/2606.11456/) (2026)
- [Synthetic Resonance: A Framework for Growth-Oriented Human-AI Relationships](https://mlsanity.com/paper/2606.18265/) (2026)
- [Is Convergence Inevitable? Tracing Output Homogeneity Back to Base Models](https://mlsanity.com/paper/2608.11426/) (2026)
- [Syntax-Guided Diffusion Language Models with User-Integrated Personalization](https://mlsanity.com/paper/2510.01028/) (2025)
- [Social Perceptions of English Spelling Variation on Twitter: A Comparative Analysis of Human and LLM Responses](https://mlsanity.com/paper/2511.23041/) (2025)
- [The Rise of Verbal Tics in Large Language Models: A Systematic Analysis Across Frontier Models](https://mlsanity.com/paper/2604.19139/) (2026)
- [Characterizing Cultural Localization in AI-Generated Stories](https://mlsanity.com/paper/2606.14626/) (2026)
- [Linguistic Monoculture in LLM-Assisted Language Use](https://mlsanity.com/paper/2607.27134/) (2026)
- [Language, Language Models, and What We're Talking About](https://mlsanity.com/paper/2609.03577/) (2026)
- [An LLM-Associated Register Shift in Korean Journal Abstracts: A Morphology-Aware Excess-Vocabulary Study, 2018-2026](https://mlsanity.com/paper/2609.07447/) (2026)
- [Ownership in AI-Assisted Everyday Tasks](https://mlsanity.com/paper/2609.20658/) (2026)
- [Agents Can Use Base Models to Evade AI Detection](https://mlsanity.com/paper/2609.31876/) (2026)
- [SlopBench: How Well Can We Rank Language Models by Slop? A Multi-Domain Benchmark of Repetitive AI Writing](https://mlsanity.com/paper/2609.33905/) (2026)
- [Measuring and Reducing Unusable Replies of LLM Agents](https://mlsanity.com/paper/2610.19659/) (2026)

## Abstract (arXiv)

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 <= .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.