Creative Homogeneity Across LLMs

• Creative homogeneity is defined as LLMs producing outputs with low semantic and stylistic variability, evidenced by similar narrative structures and evaluative scores across models.
• Empirical studies reveal that while LLMs maintain high fluency and structure in tasks like story writing and ideation, they often lack the originality and cultural nuance found in human creativity.
• Emerging methods such as leap-of-thought training and diversity-driven optimization show promising potential in mitigating homogeneity to enhance creative and semantic diversity.

Creative homogeneity across LLMs denotes the convergence of their outputs—across architectures, vendors, and deployment environments—toward remarkably similar patterns in style, content, and underlying cognitive or cultural assumptions, even in open-ended creative tasks. This phenomenon extends beyond surface-level similarities, encompassing semantic, narrative, and evaluative congruence in contexts such as story generation, creative writing, ideation, humor, and cultural representation. The mechanisms, metrics, and implications of this convergence are now the subject of multi-disciplinary investigation, including benchmark-driven comparisons, process analyses, and sociocultural critiques.

1. Defining and Measuring Creative Homogeneity

Creative homogeneity refers to the phenomenon in which the outputs of multiple independent LLMs are more similar—semantically, structurally, and stylistically—than the outputs produced by comparable populations of humans when tasked with creative generation. Empirical frameworks to quantify this include:

• Embedding-based distance and similarity metrics: For ideation tasks (e.g., the Alternative Uses Test and Divergent Association Task), pairwise cosine similarities in embedding spaces (e.g., all-MiniLM-L6-v2) systematically show lower population-level variability among LLMs than among humans (Wenger et al., 31 Jan 2025, Anderson et al., 2 Feb 2024).
• Agreement in evaluative criteria: Multiple LLMs exhibit high alignment in creativity assessments—consistent scoring and ranking (e.g., Spearman's ρ > 0.7) on standard AUT responses (Rabeyah et al., 23 Nov 2024).
• Rubric-driven story evaluation: Expert-rated story-writing evaluations find that, though fluency and structure scores are high and tightly clustered for leading commercial models, true originality and humor remain less varied than in human narratives (Gómez-Rodríguez et al., 2023, Gómez-Rodríguez et al., 22 Jun 2024, Ismayilzada et al., 4 Nov 2024).
• Latent space affinity and activation progression: Analyses of representational geometries show nearest neighbor structures are highly similar at comparable depths across LLM architectures, despite differing training sources (Wolfram et al., 3 Apr 2025).

Such convergence persists after controlling for prompt structure, verbosity, model size, domain, and even when selecting from different vendor/model families (Wenger et al., 31 Jan 2025). This indicates that creative homogeneity is not an artifact of superficial configuration, but a robust, underlying regularity.

2. Empirical Findings Across Creative Domains

Multi-domain studies establish several patterns of creative homogeneity across LLMs:

• Story Writing and Short Fiction: In tasks requiring narrative creation (e.g., a duel between Ignatius J. Reilly and a pterodactyl), leading commercial LLMs produce outputs with low standard deviation in fluency, structure, archetype, and genre adherence, with variation clustering in “technical” rather than “imaginative” traits (Gómez-Rodríguez et al., 2023, Gómez-Rodríguez et al., 22 Jun 2024). Open-source models, while less competitive overall, are also more variable but generally less capable.
• Ideation and Divergent Thinking: Tests such as the Alternative Uses Test, Forward Flow, and Divergent Association Task reveal that, while LLMs match or slightly exceed individual human “originality” scores, their population-level semantic variability is markedly lower—mean cosine distance between LLM responses is consistently and significantly less than that of human-generated outputs (Wenger et al., 31 Jan 2025).
• Creative Humor and Associative Leaps: Standard prompting and even Chain-of-Thought guidance tend to elicit routine, homogeneous humor (“safe” or expected associations). Only with explicit, non-sequential Leap-of-Thought (LoT) training (as in CLoT) do LLMs begin to break this pattern and increase output diversity (Zhong et al., 2023).
• Cultural and Social Representation: LLMs tend toward moderate, “middle ground” cultural outputs, flattening distinctions across national, demographic, and ethnic lines (Sukiennik et al., 11 Apr 2025). Studies show notably higher homogeneity (more tightly clustered embedding similarities) in LLM-generated descriptions for subordinate groups (Lee et al., 16 Jan 2024), and persistent monoculture in ethnic-occupational assignment in educational and storytelling contexts (Priyanshu et al., 11 May 2024). Even models trained in non-Western contexts more closely align to US cultural values than to their own (Sukiennik et al., 11 Apr 2025).
• Evaluation Uniformity: When LLMs themselves are tasked with creativity assessment, inter-model agreement is extremely high, regardless of whether scoring or ranking methods are used (Rabeyah et al., 23 Nov 2024). This alignment suggests that the internal evaluative criteria used by these models have also converged.

3. Causes and Mechanisms of Convergence

Multiple factors underpin creative homogeneity in LLMs:

• Overlapping Training Data: Web-scale pretraining corpora, even when vendor-specific, are heavily biased toward English-language, US/Western-centric sources and mainstream stylistic norms. Shared “guardrails” and alignment data further amplify this effect (Priyanshu et al., 11 May 2024, Sukiennik et al., 11 Apr 2025, Sourati et al., 2 Aug 2025).
• Alignment and Instruction Tuning Practices: Instruction tuning, especially with structured templates and explicit system roles, induces “diversity collapse,” where the use of rigid format tokens such as <|user|> and <|assistant|> drastically reduces semantic and lexical diversity across outputs, even at high sampling temperatures (Yun et al., 25 May 2025).
• Architectural and Representational Similarity: Across model architectures, representational progressions at comparable network depths are strongly aligned (as evidenced by near-diagonal affinity matrices across models), suggesting that all models, regardless of vendor, process language through analogous layers of abstraction (Wolfram et al., 3 Apr 2025).
• Preference Optimization Objectives: Direct preference optimization for helpfulness, informativeness, and harmlessness tends to select for outputs that are norm-consistent and risk-averse, further reducing variability unless explicit creativity signals (e.g., surprise, novelty, diversity) are injected into the training loss (Ismayilzada et al., 20 May 2025).

4. Impact on Human Ideation, Cultural Representation, and Collective Intelligence

Empirical and review findings converge on several critical consequences:

• Narrowing of Collective Expression: Recursively using LLMs as creativity support tools narrows cross-user idea diversity, even as within-user flexibility is maintained or enhanced. This generates an “algorithmic monoculture” that can crowd out nonconformist ideas and reduce the scope for unconventional breakthroughs (Anderson et al., 2 Feb 2024, Sourati et al., 2 Aug 2025).
• Cultural Flattening and Social Biases: LLM outputs frequently normalize to a global cultural median, masking or suppressing minority voices and local nuance. This standardization has measurable effects, such as reduced occupational-ethnic variability in children’s stories and a tendency to portray subordinate social groups with stereotyped sameness (Priyanshu et al., 11 May 2024, Lee et al., 16 Jan 2024, Sukiennik et al., 11 Apr 2025).
• Creative Products vs. Process: Although LLMs can match or surpass average humans in technical quality, their process shows less diversity in semantic exploration strategies (e.g., rigidly persistent or uniformly flexible pathways rather than interleaved radical jumps typical of human ideation). Aggregated LLM behavior mimics average human profiles, yet lacks the nuanced variability seen in expert or nonstandard creative populations (Nath et al., 1 May 2024, Nath et al., 1 May 2024).
• Impairment of Cognitive Diversity and Adaptability: Ubiquitous use of similar LLMs for composition, problem solving, or everyday communication may entrench dominant styles and reasoning forms at the expense of heterodox or culturally grounded approaches. This poses a risk to collective intelligence and adaptive problem solving (Sourati et al., 2 Aug 2025).

5. Methodological Innovations and Emerging Mitigations

Recent research addresses creative homogeneity through explicit interventions:

• Structured and Theory-Driven Process Engineering: Implementing frameworks such as combinatorial creativity—with multi-level abstraction retrieval and systematic recombination—encourages models to draw from disparate domains and generate ideas with higher contextual and conceptual diversity (Gu et al., 18 Dec 2024).
• Leap-of-Thought and Self-Refinement: Conditional generation methods (introduction of probabilistic noun clues and self-ranking of candidate outputs with discriminative routines) have demonstrated increased humor, novelty, and surprise in associative tasks (Zhong et al., 2023).
• Preference Optimization for Creativity: Weighted multi-dimensional losses (injecting diversity, novelty, surprise, and quality signals) in alignment objectives show statistically significant improvements on both automated and human-rated creativity tasks, demonstrably reducing output homogeneity compared to standard SFT and DPO baselines (Ismayilzada et al., 20 May 2025).
• Prompt and Format Design: Avoiding rigid, chat-style prompt templates and minimizing structural tokens during inference can mitigate diversity collapse, restoring variation in open-ended generation (Yun et al., 25 May 2025). Mixed prompt training and strategic emphasis on unstructured data are additional directions.
• Frameworks for Multi-Dimensional Reasoning: Architectures that combine Chain-of-Thought reasoning, Mixture of Experts, and semantic up/down-sampling diversify the semantic and reasoning pathways available to the model, breaking the convergence associated with linear, monolithic architectures (Tang et al., 16 Jun 2025).

6. Open Questions and Future Directions

While there is mounting evidence for creative homogeneity across LLMs, crucial research gaps remain:

• Comprehensive Modeling of Creativity Dimensions: Most large-scale studies emphasize originality and diversity but often omit elaboration, flexibility, and other facets integral to human creativity. Broadened metrics are needed (Wenger et al., 31 Jan 2025, Ismayilzada et al., 4 Nov 2024).
• Causal Attribution: Distinguishing the roles of data overlap, model architecture, alignment procedure, and prompt structure in producing homogeneity remains challenging. Future work is likely to include targeted interventions and cross-model ablation studies (Wolfram et al., 3 Apr 2025, Yun et al., 25 May 2025).
• Societal, Ethical, and Epistemic Risks: There is a need for systematic paper of the downstream effects of persistent LLM usage on cultural narratives, stereotype formation, and adaptive intelligence, particularly in education, policymaking, and creative professions (Priyanshu et al., 11 May 2024, Sourati et al., 2 Aug 2025).
• Breaking the Homogeneous Regime: Promising advances include ensemble methods (combining outputs from divergent model families), deliberate injection of randomness or pluralism into both training and inference, and pluralistic alignment strategies that incentivize structural, not just superficial, diversity (Ismayilzada et al., 20 May 2025, Tang et al., 16 Jun 2025).
• Process-Level Interpretability: Further research is required to link process-level semantic exploration in LLMs to output diversity and creative potential, possibly enabling model selection or augmentation for task-specific creative requirements (Nath et al., 1 May 2024).

7. Summary Table: Principal Findings Across Key Papers

Dimension	LLMs vs Humans	Intra-LLM Homogeneity	Mechanistic Drivers
Storywriting Originality	Lower	High (esp. commercial)	Alignment, training data
Ideation/Alternate Uses	Equal or greater (individual); less variable (group)	High	Representational similarity
Humor/Surprise	Binary split by scale	High	Model size, alignment
Cultural Representation	US/Western aligned	High	Data imbalance, guardrails
Evaluation of Creativity	High agreement	High	Standardized evaluative criteria
Mathematical Creativity	Lower on open tasks	Uniformly limited	Recombinatory pattern reliance
Effect of Prompt Structure	Reduced diversity	High with rigid formats	Structured tokens, format priors

This synthesis underscores that while contemporary LLMs exhibit technical sophistication and surface-level flexibility in creative domains, their outputs—when considered at the population or system level—tend to be far more homogeneous than those of comparable human cohorts. This tendency is anchored in overlapping data regimes, architectural regularity, and alignment mechanisms. Mitigating this phenomenon, particularly in high-stakes or innovation-critical domains, will require multi-faceted strategies spanning training, evaluation, and interface design, as well as a deepened theoretical framework for machine creativity.