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.
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.
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Hugging Face Trending Papers
Jul 29
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.
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 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
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
Jul 3
CreativityNeuro: Steering Language Model Weights to Improve Divergent Thinking and Reduce Mode Collapse
arXiv:2607. 01433v1 Announce Type: new Abstract: Divergent thinking is a crucial aspect of creativity, yet large language models (LLMs) tend to consistently generate similar responses to open-ended questions, in what has been termed the artificial hivemind effect.
arXiv AI
Jul 3
CreativityPrism: A Cross-Domain Evaluation Framework for Large Language Model Creativity
arXiv:2510. 20091v3 Announce Type: replace-cross Abstract: Creativity is often seen as a hallmark of human intelligence.
arXiv AI
Jun 11
Automated Creativity Evaluation of Language Models Across Open-Ended Tasks
arXiv:2606. 11762v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved remarkable progress in language understanding, reasoning, and generation, sparking growing interest in their creative potential.
arXiv AI
Jun 30
The Human Creativity Benchmark
arXiv:2606. 30561v1 Announce Type: new Abstract: Modern AI evaluation frameworks treat evaluator disagreement as noise to be resolved.
Hugging Face Trending Papers
Jun 10
Automated Creativity Evaluation of Language Models Across Open-Ended Tasks
Large language models (LLMs) have achieved remarkable progress in language understanding, reasoning, and generation, sparking growing interest in their creative potential. Realizing this potential requires systematic and scalable methods for evaluating creativity across diverse tasks.
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
Jul 16
The Hitchhiker's Guide to Monoculture
arXiv:2607. 13077v1 Announce Type: cross Abstract: Large language models (LLMs) often produce homogeneous outputs, raising concerns that AI coding assistants may lead to convergence in the software artifacts that developers create.