How LLMs See Creativity: Zero-Shot Scoring of Visual Creativity with Interpretable Reasoning
arXiv:2606.29672v1 Announce Type: new Abstract: Evaluating the originality of visual images poses enduring challenges for creativity assessment. Automated scoring using AI models has proven effective in the verbal domain, yet key questions remain about evaluating visual creativity and understanding how models arrive at their ratings. The present research asks whether multimodal large language models (LLMs) can serve as judges of visual creativity zero-shot (without any fine-tuning or examples of human ratings) and whether their "reasoning" output offers an interpretable window into their evalu
This research addresses the emergent capability of multimodal LLMs to not only process but also 'reason' about visual data, a critical step as these models become more sophisticated and integrated into various applications.
A strategic reader should care because this demonstrates LLMs moving beyond text generation into complex cognitive tasks like zero-shot visual creativity assessment with interpretable reasoning, impacting fields from design to advertising.
The ability of LLMs to independently evaluate and explain visual creativity could transform how visual content is generated, curated, and understood, potentially automating previously human-centric assessment processes.
- · AI developers
- · Creative industries using GenAI
- · Content moderation platforms
- · Human visual creativity assessors
- · Traditional content evaluation agencies
Multimodal LLMs gain new capabilities in understanding and evaluating visual content without prior training.
Automation of visual content analysis and feedback accelerates, impacting creative workflows and advertising.
The definition and nature of 'creativity' itself may be influenced by AI-driven assessment criteria, leading to potentially standardized creative outputs.
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Read at arXiv cs.CL