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.
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.
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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.
Hugging Face Trending Papers
Aug 4
On the Diversity of Analogy Making in Large Language Models
Large Language Models (LLMs) have demonstrated remarkable potential for analogy making, a core cognitive capability that drives novelty and creativity. While prior research has extensively investigated the applications and underlying mechanisms of LLM-based analogy making, its output diversity remains largely unexplored, despite being essential for broadening cross-domain connections and fostering scientific innovation.
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.
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
Jul 21
ReMIND: Orchestrating Modular Large Language Models for Controllable Serendipity A REM-Inspired System Design for Emergent Creative Ideation
arXiv:2601. 07121v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used not only for problem solving but also for creative ideation; however, generating ideas that are both novel and coherent remains challenging.
arXiv AI
Aug 11
Large Language Models Align with the Human Brain during Creative Thinking
arXiv:2604. 03480v2 Announce Type: replace-cross Abstract: Creative thinking is a fundamental aspect of human cognition, and divergent thinking-the capacity to generate novel and varied ideas-is widely regarded as its core generative engine.
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
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 16
LLM Jaggedness Unlocks Scientific Creativity
arXiv:2605. 10574v3 Announce Type: replace Abstract: As artificial intelligence advances, models are not improving uniformly.
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.