Creativity serves as a cornerstone for societal progress and innovation, but
its assessment remains a complex and often subjective endeavor. With the rise
of advanced generative AI models capable of tasks once reserved for human
creativity, the study of AI's creative potential becomes imperative for its
responsible development and application. This paper addresses the complexities
in defining and evaluating creativity by introducing a new concept called
Relative Creativity. Instead of trying to define creativity universally, we
shift the focus to whether AI can match the creative abilities of a
hypothetical human. This perspective draws inspiration from the Turing Test,
expanding upon it to address the challenges and subjectivities inherent in
evaluating creativity. This methodological shift facilitates a statistically
quantifiable evaluation of AI's creativity, which we term Statistical
Creativity. This approach allows for direct comparisons of AI's creative
abilities with those of specific human groups. Building on this foundation, we
discuss the application of statistical creativity in contemporary
prompt-conditioned autoregressive models. In addition to defining and analyzing
a measure of creativity, we introduce an actionable training guideline,
effectively bridging the gap between theoretical quantification of creativity
and practical model training. Through these multifaceted contributions, the
paper establishes a cohesive, continuously evolving, and transformative
framework for assessing and fostering statistical creativity in AI models.

通过引入 Relative Creativity 的概念，我们将重点转移到 AI 是否能够与理论上的人类创造能力相媲美，并基于统计学上可量化的评估，介绍了 Statistical Creativity 作为衡量 AI 创造力的方法。通过分析创造力度量，并引入可行的训练指导方针，我们建立了一个评估和促进 AI 模型中统计创造力的连贯、不断发展和变革的框架。