arXiv AI
Jul 31
Human diversity fuels collective creativity that large language models cannot simulate or sustain
arXiv:2607. 26899v1 Announce Type: cross Abstract: Diverse human groups produce diverse ideas, the raw material of innovation.
Human diversity fuels collective creativity that large language models cannot simulate or sustain
Diverse human groups produce diverse ideas, the raw material of innovation. Generative AI challenges this engine twice over: everyday AI assistance may homogenize what diverse people create, and AI-simulated diversity may replace the people altogether.
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arXiv AI
Sep 3
Collective creativity in hybrid societies
The article discusses how generative AI is reshaping the creation and circulation of cultural artifacts, prompting debate over whether these tools enrich or impoverish culture. It distinguishes between novelty—an attribute of individual artifacts—and diversity—an attribute of populations—arguing that creativity should be viewed as a property of hybrid collectives composed of people and algorithms. The authors find that AI-assisted ideation increases the novelty of individual outputs while potentially narrowing overall diversity, but that mixed human–machine groups can outperform single-type groups and sustain machine-generated solutions within human culture, depending on the composition and connectivity of the agents involved.
arXiv AI
Jun 9
Seeing the Hivemind: A Consensus-Aware Interaction Technique for Mitigating AI Homogenization
arXiv:2606. 09587v1 Announce Type: cross Abstract: People are increasingly using AI for creative tasks such as writing.
arXiv AI
6d ago
Breaking Homogeneity: Diversifying Persona Sets for Creative LLM Outputs
The paper introduces persona diversification as a set‑level conditioning strategy to reduce homogeneity in large language model outputs. It explores two design axes—selecting versus generating personas and space‑filling versus frontier‑seeking diversity—implementing four methods that span coverage and dispersion subset selections, uniform‑coverage sampling, and evolutionary persona generation. Experiments on tasks such as the Alternative Uses Task, Infinity‑Chat, and Divergent Association Task demonstrate significant gains in response diversity, originality, flexibility, and overall creativity, while maintaining high validity.
arXiv AI
Aug 5
Assessing the Effect of Cross-Domain Mapping on Creativity in Humans and Large Language Models
arXiv:2603. 19087v2 Announce Type: replace Abstract: Creativity is the ability to come up with novel ideas, a capacity crucial for human development and flourishing.
arXiv AI
Aug 11
Harnessing Abundance: A Generativity Perspective on Human-GenAI Collaboration
arXiv:2608. 07500v1 Announce Type: cross Abstract: Research on human-GenAI collaboration yields conflicting findings: GenAI can enhance creativity yet reduce collective diversity, with uneven benefits across skill levels.
arXiv Computation and Language
Sep 2
Creative Generation via Multi-Agent Debate: Does Debate Suppress Diversity?
The paper investigates the use of Multi-Agent Debate (MAD) for creative generation tasks such as narrative writing and scientific ideation. It finds that MAD’s convergence-driven design suppresses output diversity across independent runs, creating a trade-off with creative tasks. To address this, the authors propose Creative-MAD, which introduces Cognitive Lens Assignment and Embedding-based Peer Selection to preserve agent divergence, and demonstrate that it improves lexical and semantic diversity while maintaining quality.
arXiv AI
Jun 6
Individual Gain, Collective Loss: Metacognitive Adaptation in AI-Assisted Creativity
arXiv:2606. 05532v1 Announce Type: new Abstract: Recent studies reveal a paradox: AI enhances individual creative outputs while reducing collective diversity.
arXiv AI
Aug 21
Are LLMs becoming similarly creative? Evidence from three years of models
arXiv:2608. 19437v1 Announce Type: cross Abstract: Many benchmarks track Large Language Model (LLM) performance on tasks with verifiable answers, but less is known about how LLM performance is evolving on open-ended tasks, where creativity, originality and diversity may matter as much as quality.
arXiv AI
3d ago
Who Owns That? Evaluating Ownership Intuitions in Large Language Models
arXiv:2609.39483v1 Announce Type: new Abstract: Ownership establishes rights over the use, control, and transfer of objects. Understanding these relations is essential for AI systems to interact appr...
arXiv AI
Jun 11
Nonslop: A Gamified Experiment in Human-AI Collaborative Writing
arXiv:2606. 12350v1 Announce Type: new Abstract: The rapid proliferation of large language models (LLMs) raises critical questions about human creativity and individual expression in an era of AI-assisted creation.
arXiv AI
Aug 10
CreativeInstruct: Scalably Teaching LLMs to Balance Quality, Creativity, and Diversity
arXiv:2608. 07460v1 Announce Type: cross Abstract: While post-training improves the capabilities of large language models (LLMs), it generally lowers their output diversity and creativity, negatively impacting tasks that explicitly require creativity (e.