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Rethinking Creativity Evaluation: A Critical Analysis of Existing Creativity Evaluations

Published: August 7, 2025 | arXiv ID: 2508.05470v1

By: Li-Chun Lu , Miri Liu , Pin-Chun Lu and more

Potential Business Impact:

Helps computers judge creative ideas more like humans.

We systematically examine, analyze, and compare representative creativity measures--creativity index, perplexity, syntactic templates, and LLM-as-a-Judge--across diverse creative domains, including creative writing, unconventional problem-solving, and research ideation. Our analyses reveal that these metrics exhibit limited consistency, capturing different dimensions of creativity. We highlight key limitations, including the creativity index's focus on lexical diversity, perplexity's sensitivity to model confidence, and syntactic templates' inability to capture conceptual creativity. Additionally, LLM-as-a-Judge shows instability and bias. Our findings underscore the need for more robust, generalizable evaluation frameworks that better align with human judgments of creativity.

Country of Origin
🇺🇸 United States

Page Count
15 pages

Category
Computer Science:
Computation and Language