mlsanityData to 14 Oct 2026

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

arXiv 2508.01491 ↗First posted 2 Aug 2025cs.CLcited by 35

On the map: LLM reasoning → Reasoning benchmarks and evaluation

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.

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.

Builds on

114 of 214 references.

Closest ancestors · 8

Same direction, newest first

  1. 2025Do Multilingual LLMs Think In English?21 Feb
  2. 2025Reasoning on a Spectrum: Aligning LLMs to System 1 and System 2 Thinking18 Feb
  3. 2024Beware of Words: Evaluating the Lexical Diversity of Conversational LLMs using ChatGPT as Case Study11 Feb
  4. 2024What Large Language Models Know and What People Think They Know24 Jan
  5. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debate23 May
  6. 2023Diminished Diversity-of-Thought in a Standard Large Language Model13 Feb
  7. 2022Emergent Analogical Reasoning in Large Language Models19 Dec
  8. 2021Show Your Work: Scratchpads for Intermediate Computation with Language Models30 Nov

Other references · 106

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2024The Llama 3 Herd of Modelscited by 14,715
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  5. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  6. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  7. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,780
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  9. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
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  11. 2019The Curious Case of Neural Text Degenerationcited by 2,916
  12. 2023A Survey of Large Language Modelscited by 2,576
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  9. 2018Texygen: A Benchmarking Platform for Text Generation Modelscited by 491
  10. 2025Stop Overthinking: A Survey on Efficient Reasoning for Large Language Modelscited by 464
  11. 2023Benchmarking Large Language Models for News Summarizationcited by 445
  12. 2023Towards Measuring the Representation of Subjective Global Opinions in Language Modelscited by 437
  13. 2022Using cognitive psychology to understand GPT-3cited by 398
  14. 2023Understanding the Effects of RLHF on LLM Generalisation and Diversitycited by 396
  15. 2023On the Planning Abilities of Large Language Models : A Critical Investigationcited by 312
  16. 2024Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoningcited by 280
  17. 2023The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learningcited by 257
  18. 2024On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Surveycited by 244
  19. 2024Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?cited by 232
  20. 2023Evaluating Large Language Models in Theory of Mind Taskscited by 222
  21. 2023Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Modelscited by 207
  22. 2023The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientationcited by 189
  23. 2024A Roadmap to Pluralistic Alignmentcited by 184
  24. 2024A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systemscited by 170
  25. 2023Generative artificial intelligence enhances creativity but reduces the diversity of novel contentcited by 167
  26. 2023Fundamental Limitations of Alignment in Large Language Modelscited by 155
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  29. 2022The 'Problem' of Human Label Variation: On Ground Truth in Data, Modeling and Evaluationcited by 139
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  31. 2019Unifying Human and Statistical Evaluation for Natural Language Generationcited by 127
  32. 2024Homogenization Effects of Large Language Models on Human Creative Ideationcited by 127
  33. 2023Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordiacited by 126
  34. 2021How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVENcited by 124
  35. 2023Language Model Crossover: Variation through Few-Shot Promptingcited by 122
  36. 2022Jury Learning: Integrating Dissenting Voices into Machine Learning Modelscited by 118
  37. 2024Mapping the Increasing Use of LLMs in Scientific Paperscited by 118
  38. 2024Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worsecited by 109

50 most cited of 106 All 106 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 32.

35 papers.

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  1. 2025What and Whose Knowledge? Measuring Epistemic Diversity in Large Language Modelscited by 14
  2. 2026Measuring the Gap Between Human and LLM Research Ideascited by 10
  3. 2026LLMs Exhibit Significantly Lower Uncertainty in Creative Writing Than Professional Writerscited by 4
  4. 2026PERSPECTRA: A Scalable and Configurable Pluralist Benchmark of Perspectives from Argumentscited by 3
  5. 2026AI generates well-liked but templatic empathic responsescited by 3
  6. 2026The Homogenization Problem in LLMs: Towards Meaningful Diversity in AI Safetycited by 2
  7. 2026The Collapse of Heterogeneity in Silicon Philosopherscited by 2
  8. 2026The Deliberative Deficit: An Empirical Critique of LLMs in Democratic Discoursecited by 2
  9. 2026Most biomedical publications show signs of LLM-assisted writingcited by 2
  10. 2025Realistic threat perception drives intergroup conflict: A causal, dynamic analysis using generative-agent simulationscited by 1
  11. 2025Toward Human-Centered AI-Assisted Terminology Workcited by 1
  12. 2026Group Selection as a Safeguard Against AI Substitutioncited by 1
23 more
  1. 2026Brief chatbot interactions produce lasting changes in human moral valuescited by 1
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  3. 2026Ex Ante Evaluation of AI-Induced Idea Diversity Collapsecited by 1
  4. 2026emb-diversity: A Tool for Embedding-Based Measurement of Data Diversitycited by 1
  5. 2026Forty Shades of Blue: Quality-Diversity Alignment via Mode-Conditioned Reinforcement Learningcited by 1
  6. 2026BRIDGE the Gap: Mitigating Bias Amplification in Automated Scoring of English Language Learners via Inter-group Data Augmentationcited by 0
  7. 2026Agentivism: a learning theory for the age of artificial intelligencecited by 0
  8. 2026From Context Shift to Stylistic Collapse: Why Training Objectives Matter More Than Scalecited by 0
  9. 2026Do Large Language Models Encode Institutional Experience? Evidence from Cross-Linguistic Moral Reasoning Under Ambiguitycited by 0
  10. 2026Are LLMs ready for HardChoices?cited by 0
  11. 2026Introducing Human-Centeredness in AI-Assisted Lexicographycited by 0
  12. 2026The Hitchhiker's Guide to Monoculture: AI Homogenizes Syntax, Not (Necessarily) Semanticscited by 0
  13. 2026Human diversity fuels collective creativity that large language models cannot simulate or sustaincited by 0
  14. 2026Linguistic Monoculture in LLM-Assisted Language Usecited by 0
  15. 2026Rethinking Generative AI Literacy: An Integrative, Developmental, and Dialectical Framework for K-12 Teacher Educationcited by 0
  16. 2026XTC: Head-Aware Sampling by Excluding Top Choicescited by 0
  17. 2026Where Should Language Sit in a Multimodal Model? Lessons from What Language Does to Human Perception and Cognitioncited by 0
  18. 2026Spook the Machine: Gamified Exploration of Human Imagination of Machine Fearcited by 0
  19. 2026Fine-Tuning Fixes Mode Collapse and Over-Dispersion in LLMscited by 0
  20. 2026AI Should Facilitate Democratic Deliberation at Scalecited by 0
  21. 2026Fair Like Us? Auditing LLM Alignment in Resource Allocationcited by 0
  22. 2026Narrowing the Horizon: Quantifying Topic Saliency Shifts in Generative Monoculturecited by 0
  23. 2026Reach Into The CHOIR: Free-List Elicitation Uncovers Distinct Model Voices in LLM Ensemblescited by 0

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