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

Zhivar Sourati, Alireza S. Ziabari, Morteza Dehghani

arXiv:2508.01491v2cs.CL

TL;DR

Cognitive diversity supports adaptability and creativity, but LLMs may favor dominant patterns across writing, problem-solving, and conceptual exploration. The paper reports reduced stylistic and lexical diversity, recalibrated attitudes and framing, and diminished ideational diversity, while highlighting unresolved long-term effects.

  • Problem

    Cognitive diversity is essential to adaptability and creativity, yet LLM fluency and reliance on optimization raise concerns about reduced diversity in language and reasoning.

  • Method

    The paper examines how LLM outputs in writing, problem-solving, and conceptual exploration favor particular patterns.

  • Results

    The evidence indicates reduced stylistic and lexical diversity, subtle recalibration of user attitudes and framing, and diminished diversity in ideation.

  • Takeaways & Limitations

    Preserving diversity requires incorporating these insights into LLM design and distinguishing authentic human-grounded diversity from superficial synthetic variation.

  • Takeaways & Limitations

    Longitudinal evidence is lacking on whether sustained LLM reliance changes abstraction, memory retention, and reasoning strategies over time or whether such changes are irreversible.

Abstract

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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.

When LLMs Meet Human Diversity in Expression and Thought

Human diversity in language, perspective, and reasoning supports creativity, adaptability, and collective functioning, but LLMs may standardize how people express and develop ideas. Their broad integration raises concerns that shared models could reinforce dominant reasoning patterns and marginalize alternative voices.

  • Cognitive diversity arises from distinct backgrounds, linguistic repertoires, and value systems, supporting innovation and collective systems’ effectiveness.The paper links preserved distinctions to innovation, prevention of epistemic collapse, and operational efficacy.
  • LLMs increasingly participate in writing, sociocognitive modeling, psychological simulation, problem-solving, and perspective-taking, expanding their role in representing human diversity.Their integration makes it important to examine whether they preserve or enforce cognitive and linguistic diversity.
  • Homogenization may constrain public discourse, reduce visibility of marginal linguistic forms, reinforce dominant reasoning templates, and suppress idiosyncratic language signaling group perspectives.The paper also identifies a risk that linear, explicit chain-of-thought prompting may disincentivize abstract or intuitive reasoning styles.
  • LLMs differ from earlier cognitive extensions because they generate complete reasoning and articulation processes on users’ behalf, potentially relocating cognition outside individual minds.Their fluency can also lead users to overtrust responses, collapsing the boundary between external tool and internal cognition.
  • The paper synthesizes linguistics, psychology, computer science, and cognitive science across stylistic variation, perspective, and reasoning strategies to examine how LLMs reflect and influence human diversity.It argues for greater incorporation of human-grounded diversity within these dimensions.
  • Shared LLM mediation can homogenize users’ linguistic, perspectival, and reasoning signals, producing standardized expressions and thoughts across users.The paper frames this convergence as a consequence of many individuals relying on the same models across contexts.

Language Models, Prediction, and the Loss of Diversity

LLMs predict language from statistical regularities in large training corpora, but this process can privilege dominant patterns and narrow expressive and conceptual diversity. Their widespread use may reinforce this narrowing as model outputs re-enter human communication and future training data.

  • Model foundations: LLMs extend next-token prediction from earlier statistical language models by training on massive datasets with billions of parameters and substantial compute.Their fundamental objective remains mastering statistical regularities of language.
  • Sources of narrowing: Training data overrepresenting dominant languages and ideologies leads outputs to mirror a narrow and skewed slice of human experience.Training also favors frequent and easily generalizable patterns while smoothing over minority representations.
  • Effects on expression and thought: At scale, statistical pattern learning privileges central tendencies while marginalizing rare expressions, alternative reasoning styles, and culturally specific voices.The resulting narrowing affects not only surface linguistic form but also the conceptual space in which models write, speak, and reason.
  • Effects on expression and thought: This narrowing is historically uneven, reflecting norms and perspectives associated with English-speaking, Global North, and socioeconomically advantaged populations.Models tend to reproduce mainstream, institutionally validated perspectives and can frame them as default standards of clarity or intelligence.
  • Representation: Identity prompting can produce stereotyped out-group personas rather than authentic in-group representations, reducing varied group experiences to essentialized caricatures.One example portrays a person with impaired vision through inability to visually observe a border or read statistics.
  • Feedback loop: A recursive feedback loop makes homogenization structurally reinforced: users absorb common model patterns, and their altered expression influences future training data.This transforms homogenization from a passive bias into an active influence on human discourse and model development.

Language Diversity

LLMs can reduce socially meaningful linguistic variation even while producing fluent, polished text. Evidence across writing tasks indicates convergence in style and meaning, with diversity-reducing effects that persist across prompting and training interventions.

  • Foundations: Human linguistic variation encodes social, cultural, and individual differences through semantic, stylistic, and structural cues.Earlier NLP work sought more diverse generation, while sociolinguistics studied linguistic signatures of social position and individual traits.
  • Core finding: LLMs do not reliably preserve socially meaningful linguistic variation or links between language and speaker traits despite mastering surface-level fluency.This motivates the question of whether their variation is human-like rather than merely fluent.
  • Writing convergence: LLM-assisted polishing makes Reddit posts, news articles, academic abstracts, and personal essays converge in writing complexity, weakening predictability of author characteristics.Reportedly weakened associations include links between complex-word usage and openness to experience.
  • Writing convergence: LLM-generated college admission essays show high semantic and lexical similarity across samples, indicating a narrowing of expressive space.This homogenization persists despite temperature scaling and prompting techniques that simulate different author identities or personas.
  • Optimization trade-offs: Reinforcement-learning methods can improve reasoning while reducing stylistic and expressive variability, and continued optimization for quality may further diminish diversity.The paper frames this as a quality–novelty trade-off because novelty-promoting methods can compromise coherence.
  • Mitigation: Prompting and training strategies aim to increase diversity, but evidence from sociolinguistic contexts still calls for evaluating whether they produce genuine, context-grounded variation.Examples include persona conditioning, multiple valid responses per prompt, and diversity-aware preference weighting.
  • Everyday use: As LLMs enter everyday writing, they promote uniform styles that can mask authentic voices and reduce variation in tone, culture, and identity.This effect is reported even among people who merely engage with AI-generated text.
  • Consequences: Standardizing distinctive linguistic markers could erase early indicators used for diagnosis and intervention, including markers associated with Alzheimer’s disease.The paper also notes that LLM-incorporated styles reproduce dominant expressive norms while eroding minority and underrepresented voices.

Perspectival Diversity

LLMs show reduced variability in perspectives and values compared with humans, often aligning more closely with WEIRD societies and underrepresenting non-WEIRD viewpoints. Prompting and fine-tuning can broaden outputs, but their alignment with authentic pluralism remains uncertain, while use of LLMs can also shift users’ framing and opinions.

  • Limits of diversification: Although models can simulate diverse viewpoints through prompting, outputs often fail to match referenced groups’ actual perspective distributions.Identity-coded instructions, generation-parameter changes, and translation can improve apparent diversity without reproducing group-level distributions.
  • Limits of diversification: Such interventions may produce socially “correct” or average responses and can reproduce out-group stereotypes or misrepresentations.The reported outputs can capture the mean of a distribution rather than its contextual depth.
  • Influence on users: LLMs increasingly influence how people frame and articulate their own perspectives as they become embedded in daily communication and belief-reporting.The paper connects this influence to perspective gathering, open-ended survey responses, and users’ framing of the world.
  • Influence on users: Participants who co-wrote with opinionated language models mirrored the models’ stance and later shifted their attitudes in surveys.The effect occurred when models were engineered to frame social media positively or negatively.
  • Influence on users: Even subtle interaction can lead users to adopt a model’s framing without awareness, raising concerns about persuasive influence.The concern follows from observed stance mirroring and subsequent attitude changes.

Reasoning Diversity

LLMs often cluster around central reasoning tendencies rather than the variability found in human thought, potentially weakening the adaptive and collective benefits of cognitive diversity. Their performance-oriented objectives and widespread use can reinforce uniform reasoning, although assistance may simultaneously increase idea elaboration while reducing originality.

  • Value of reasoning diversity: Human reasoning diversity supports collective strength because groups using distinct heuristics and problem representations outperform more homogeneous groups.The passage links variation in reasoning to collective performance across disciplines.
  • LLM reasoning patterns: LLMs often align with human reasoning outcomes while producing reasoning patterns that cluster around central tendencies and lack natural variability.This discrepancy appears across tasks traditionally used to assess human cognition.
  • LLM reasoning patterns: In wisdom-of-the-crowds tasks, LLMs converge on uniform, idealized responses and miss variance arising from cultural and individual differences.Human judgment diversity helps groups approximate correct answers, whereas model uniformity omits that variance.
  • Sources of homogenization: Performance-focused training and evaluation prioritize correctness and utility over variation in reasoning approaches, while chain-of-thought techniques can reinforce homogenization.The objectives emphasize accuracy, informativeness, helpfulness, harmlessness, and consistency.
  • Sources of homogenization: Chain-of-thought prompting made GPT-4o four times slower to learn correct labels when exceptions violated a dominant vehicle-classification rule.Step-by-step reasoning overgeneralized from regular patterns and overlooked exceptions and contextual cues.
  • Effects on ideation: ChatGPT assistance produced more numerous and elaborate ideas, especially for less experienced or less creative writers, but made outputs more semantically similar.The findings indicate a trade-off between elaboration and cross-participant convergence.
  • Effects on ideation: Users often select model-suggested continuations rather than steering generation, shifting agency toward the model; effects on creativity are strongest when use begins early in ideation.The paper also reports reduced neural coupling, memory recall, and ownership in LLM-assisted writing.

Concluding remarks

The paper frames LLM homogenization as a social, cognitive, and political risk: models offer fluency and consistency while potentially displacing situated forms of thought. It reviews diversification strategies but concludes that persistent homogenization and pretraining constraints require more comprehensive evaluation and deliberate preservation of human pluralism.

  • Conclusion: Empirical evidence spans reduced lexical and stylistic diversity, recalibrated user attitudes and framing, and diminished ideation diversity.The conclusion synthesizes effects across language production, communication, and creativity.
  • Broader implications: LLMs can favor efficiency, predictability, and control, offering fluency and consistency while displacing situated, idiosyncratic forms of thought.The paper connects this pattern to Ritzer’s “McDonaldization” theory.
  • Broader implications: Centralized control of the algorithms and datasets behind LLMs concentrates political power and can enable top-down homogenization.The paper links this risk to the dominance of a few influential platforms.
  • Broader implications: The paper cites politically sensitive refusals by China’s Qwen model as evidence of systemic risks from algorithmic and market-driven control.The example concerns censorship of politically sensitive questions.
  • Potential responses: Proposed countermeasures include personalized models, embodied reasoning, diversified prompting, and multi-agent debate systems.These approaches aim to promote personalization, context sensitivity, or broader reasoning.
  • Potential responses: Evidence of persistent homogenization suggests diversification solutions must be applied more comprehensively and systematically before their effectiveness can be meaningfully evaluated.Prompting- and training-based methods remain constrained by underlying pretraining representations.
  • Potential responses: Diversification may involve trade-offs because variation far from pretraining distributions can increase hallucination likelihood.The paper identifies this as a limitation of moving beyond learned distributions.
  • Conclusion: Preserving meaningful human diversity should be a central criterion in LLM development and evaluation.The paper presents pluralism as necessary for realizing language technologies without sacrificing human diversity.

Outstanding Questions

The paper identifies unresolved questions about whether current alignment methods can reproduce deep human diversity, how to distinguish meaningful diversity from superficial variation, and how sustained LLM reliance changes cognition. It also calls for interventions that preserve user agency and establish safeguards at behavioral, architectural, and institutional levels.

  • Open research questions: It remains unclear whether supervised fine-tuning and RLHF can reproduce full cognitive diversity or require changes to architecture, objectives, and training data.These methods have increased steerability and surface-level variation, but deeper context-sensitive and culturally grounded diversity remains uncertain.
  • Open research questions: Future research should distinguish synthetic variation from diversity grounded in authentic sociocultural, emotional, and cognitive nuance.The paper calls for metrics and frameworks to make this distinction.
  • Open research questions: Longitudinal studies are needed to assess whether sustained LLM use changes abstraction, memory retention, and reasoning strategies, including possible irreversibility.Short-term reductions in stylistic variation and creative ownership have already been observed.
  • Open research questions: Behavioral and interface interventions could preserve agency by delaying LLM use during ideation or exposing users to model-induced changes.The paper presents these as strategies requiring development and evaluation.
  • Open research questions: A systematic taxonomy of behavioral, architectural, and institutional safeguards is needed to mitigate homogenization at scale.Such a taxonomy would guide users, developers, and platforms toward cognitive and linguistic pluralism.

Glossary

The glossary defines prompting, training, reasoning, language-processing, and identity-related concepts used to discuss language-model behavior and cognitive diversity.

  • Prompting and training: Chain-of-Thought prompting encourages models to show step-by-step reasoning before answering, improving structure and accuracy.
  • Model behavior and cognition: Epistemic concerns knowledge and justified belief; epistemic collapse describes lost diversity in knowledge, reasoning, or interpretation, producing more uniform thought.
  • Identity and representation: Essentialized Representations of Identity simplify social or cultural groups as fixed and homogeneous, overlooking internal variation.
  • Prompting and training: Natural Language Processing focuses on enabling computers to understand, interpret, and produce human language across tasks such as translation, summarization, and question answering.
  • Prompting and training: Prompts guide language-model behavior and output, while Reinforcement Learning from Human Feedback uses evaluator ratings to encourage preferred or context-appropriate responses.
  • Prompting and training: Supervised Fine-Tuning adapts a pre-trained model to a task or domain by continuing training on a smaller labeled dataset to teach new knowledge or behaviors.
  • Model behavior and cognition: Temperature Scaling controls output determinism or randomness: lower temperatures yield more precise, predictable text, whereas higher temperatures produce more variable responses.
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