Probing and Inducing Combinational Creativity in Vision-Language Models

• URL: http://arxiv.org/abs/2504.13120v2
• Date: Tue, 29 Apr 2025 14:51:47 GMT
• Title: Probing and Inducing Combinational Creativity in Vision-Language Models
• Authors: Yongqian Peng, Yuxi Ma, Mengmeng Wang, Yuxuan Wang, Yizhou Wang, Chi Zhang, Yixin Zhu, Zilong Zheng,
• Abstract summary: Recent advances in Vision-Language Models (VLMs) have sparked debate about whether their outputs reflect combinational creativity.<n>We propose the Identification-Explanation-Implication (IEI) framework, which decomposes creative processes into three levels.<n>To validate this framework, we curate CreativeMashup, a high-quality dataset of 666 artist-generated visual mashups annotated according to the IEI framework.
• Score: 52.76981145923602
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ability to combine existing concepts into novel ideas stands as a fundamental hallmark of human intelligence. Recent advances in Vision-Language Models (VLMs) like GPT-4V and DALLE-3 have sparked debate about whether their outputs reflect combinational creativity--defined by M. A. Boden (1998) as synthesizing novel ideas through combining existing concepts--or sophisticated pattern matching of training data. Drawing inspiration from cognitive science, we investigate the combinational creativity of VLMs from the lens of concept blending. We propose the Identification-Explanation-Implication (IEI) framework, which decomposes creative processes into three levels: identifying input spaces, extracting shared attributes, and deriving novel semantic implications. To validate this framework, we curate CreativeMashup, a high-quality dataset of 666 artist-generated visual mashups annotated according to the IEI framework. Through extensive experiments, we demonstrate that in comprehension tasks, best VLMs have surpassed average human performance while falling short of expert-level understanding; in generation tasks, incorporating our IEI framework into the generation pipeline significantly enhances the creative quality of VLMs' outputs. Our findings establish both a theoretical foundation for evaluating artificial creativity and practical guidelines for improving creative generation in VLMs.

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