# The Homogenizing Effect of Large Language Models on Human Expression and Thought

arXiv: https://arxiv.org/abs/2508.01491 · HTML: https://mlsanity.com/paper/2508.01491/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 114 of its 214 references that are in the map.
- First posted on arXiv 2 Aug 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 30 of 114)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://mlsanity.com/paper/2305.01210/) (2023)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Show Your Work: Scratchpads for Intermediate Computation with Language Models](https://mlsanity.com/paper/2112.00114/) (2021)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training](https://mlsanity.com/paper/2501.17161/) (2025)
- [Improving alignment of dialogue agents via targeted human judgements](https://mlsanity.com/paper/2209.14375/) (2022)
- [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022)
- [Texygen: A Benchmarking Platform for Text Generation Models](https://mlsanity.com/paper/1802.01886/) (2018)
- [Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models](https://mlsanity.com/paper/2503.16419/) (2025)
- [Benchmarking Large Language Models for News Summarization](https://mlsanity.com/paper/2301.13848/) (2023)
- [Towards Measuring the Representation of Subjective Global Opinions in Language Models](https://mlsanity.com/paper/2306.16388/) (2023)
- [Using cognitive psychology to understand GPT-3](https://mlsanity.com/paper/2206.14576/) (2022)
- [Understanding the Effects of RLHF on LLM Generalisation and Diversity](https://mlsanity.com/paper/2310.06452/) (2023)
- [On the Planning Abilities of Large Language Models : A Critical Investigation](https://mlsanity.com/paper/2305.15771/) (2023)
- [Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning](https://mlsanity.com/paper/2403.16999/) (2024)

## Built on by (most cited first, 30 of 35)

- [What and Whose Knowledge? Measuring Epistemic Diversity in Large Language Models](https://mlsanity.com/paper/2510.04226/) (2025)
- [Measuring the Gap Between Human and LLM Research Ideas](https://mlsanity.com/paper/2607.01233/) (2026)
- [LLMs Exhibit Significantly Lower Uncertainty in Creative Writing Than Professional Writers](https://mlsanity.com/paper/2602.16162/) (2026)
- [PERSPECTRA: A Scalable and Configurable Pluralist Benchmark of Perspectives from Arguments](https://mlsanity.com/paper/2602.08716/) (2026)
- [AI generates well-liked but templatic empathic responses](https://mlsanity.com/paper/2604.08479/) (2026)
- [The Homogenization Problem in LLMs: Towards Meaningful Diversity in AI Safety](https://mlsanity.com/paper/2601.06116/) (2026)
- [The Collapse of Heterogeneity in Silicon Philosophers](https://mlsanity.com/paper/2604.23575/) (2026)
- [The Deliberative Deficit: An Empirical Critique of LLMs in Democratic Discourse](https://mlsanity.com/paper/2608.10186/) (2026)
- [Most biomedical publications show signs of LLM-assisted writing](https://mlsanity.com/paper/2608.10715/) (2026)
- [Realistic threat perception drives intergroup conflict: A causal, dynamic analysis using generative-agent simulations](https://mlsanity.com/paper/2512.17066/) (2025)
- [Toward Human-Centered AI-Assisted Terminology Work](https://mlsanity.com/paper/2512.18859/) (2025)
- [Group Selection as a Safeguard Against AI Substitution](https://mlsanity.com/paper/2602.03541/) (2026)
- [Brief chatbot interactions produce lasting changes in human moral values](https://mlsanity.com/paper/2604.21430/) (2026)
- [How AI Writing Assistance Distorts Perceptions of AI Users and Their Opinions](https://mlsanity.com/paper/2604.22503/) (2026)
- [Ex Ante Evaluation of AI-Induced Idea Diversity Collapse](https://mlsanity.com/paper/2605.06540/) (2026)
- [emb-diversity: A Tool for Embedding-Based Measurement of Data Diversity](https://mlsanity.com/paper/2607.19848/) (2026)
- [Forty Shades of Blue: Quality-Diversity Alignment via Mode-Conditioned Reinforcement Learning](https://mlsanity.com/paper/2609.14896/) (2026)
- [BRIDGE the Gap: Mitigating Bias Amplification in Automated Scoring of English Language Learners via Inter-group Data Augmentation](https://mlsanity.com/paper/2602.23580/) (2026)
- [Agentivism: a learning theory for the age of artificial intelligence](https://mlsanity.com/paper/2604.07813/) (2026)
- [From Context Shift to Stylistic Collapse: Why Training Objectives Matter More Than Scale](https://mlsanity.com/paper/2605.28826/) (2026)
- [Do Large Language Models Encode Institutional Experience? Evidence from Cross-Linguistic Moral Reasoning Under Ambiguity](https://mlsanity.com/paper/2605.30934/) (2026)
- [Are LLMs ready for HardChoices?](https://mlsanity.com/paper/2607.11471/) (2026)
- [Introducing Human-Centeredness in AI-Assisted Lexicography](https://mlsanity.com/paper/2607.11808/) (2026)
- [The Hitchhiker's Guide to Monoculture: AI Homogenizes Syntax, Not (Necessarily) Semantics](https://mlsanity.com/paper/2607.13077/) (2026)
- [Human diversity fuels collective creativity that large language models cannot simulate or sustain](https://mlsanity.com/paper/2607.26899/) (2026)
- [Linguistic Monoculture in LLM-Assisted Language Use](https://mlsanity.com/paper/2607.27134/) (2026)
- [Rethinking Generative AI Literacy: An Integrative, Developmental, and Dialectical Framework for K-12 Teacher Education](https://mlsanity.com/paper/2608.01705/) (2026)
- [XTC: Head-Aware Sampling by Excluding Top Choices](https://mlsanity.com/paper/2608.22758/) (2026)
- [Where Should Language Sit in a Multimodal Model? Lessons from What Language Does to Human Perception and Cognition](https://mlsanity.com/paper/2609.07474/) (2026)
- [Spook the Machine: Gamified Exploration of Human Imagination of Machine Fear](https://mlsanity.com/paper/2609.15472/) (2026)

## Abstract (arXiv)

Cognitive diversity, reflected in variations of language, perspective, and reasoning, is essential to creativity and collective intelligence. This diversity is rich and grounded in culture, history, and individual experience. Yet as large language models (LLMs) become deeply embedded in people's lives, they risk standardizing language and reasoning. We synthesize evidence across linguistics, psychology, cognitive science, and computer science to show how LLMs reflect and reinforce dominant styles while marginalizing alternative voices and reasoning strategies. We examine how their design and widespread use contribute to this effect by mirroring patterns in their training data and amplifying convergence as all people increasingly rely on the same models across contexts. Unchecked, this homogenization risks flattening the cognitive landscapes that drive collective intelligence and adaptability.