arXiv:2510.20091v3 Announce Type: replace Abstract: Creativity is often seen as a hallmark of human intelligence. While large language models(LLMs) are increasingly perceived as generating creative text, there is still no cross-domain and scalable framework to evaluate their creativity across diverse scenarios. Existing methods of LLM creativity evaluation either heavily rely on humans, limiting speed and scalability, or are fragmented across different domains and different definitions of creativity. To address this gap, we propose CreativityPrism, an evaluation and analysis framework that con
As LLMs become more sophisticated and widely deployed, the urgent need for standardized, scalable, and cross-domain evaluation metrics for 'creativity' is paramount to guide development and assess capabilities.
A robust framework for evaluating LLM creativity will accelerate AI development by providing clear benchmarks, enabling better model comparison, and informing investment in novel AI architectures.
The introduction of CreativityPrism offers a potential standardized metric, moving away from fragmented, human-reliant evaluations to a more scalable and objective assessment of LLM creative output.
- · AI researchers and developers
- · Companies building foundation models
- · Businesses leveraging LLMs for creative tasks
- · Open-source LLM communities
- · Proprietary evaluation services
- · LLM developers without robust internal evaluation methods
Standardized creativity metrics will lead to faster iteration and improvement in large language models designed for creative tasks.
Improved creative LLMs could disrupt industries like content creation, advertising, and design, potentially leading to fully autonomous creative AI agents.
The definition of human creativity might be re-evaluated as AI models demonstrate consistently measurable and high-quality creative outputs, blurring the lines further.
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