@inproceedings{li-etal-2025-automated-creativity,
    title = "Automated Creativity Evaluation for Large Language Models: A Reference-Based Approach",
    author = "Li, Ruizhe  and
      Zhu, Chiwei  and
      Xu, Benfeng  and
      Wang, Xiaorui  and
      Mao, Zhendong",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.1171/",
    doi = "10.18653/v1/2025.findings-emnlp.1171",
    pages = "21475--21488",
    ISBN = "979-8-89176-335-7",
    abstract = "Creative writing is a key capability of Large Language Models (LLMs), with potential applications in literature, storytelling, and various creative domains. However, evaluating the creativity of machine-generated texts remains a significant challenge, as existing methods either rely on costly manual annotations or fail to align closely with human assessments. In this paper, we propose an effective automated evaluation method based on the Torrance Test of Creative Writing (TTCW), which evaluates creativity as product. Our method employs a reference-based Likert-style approach, scoring generated creative texts relative to high-quality reference texts across various tests. Experimental results demonstrate that our method significantly improves the alignment between LLM evaluations and human assessments, achieving a pairwise accuracy of 0.75 (+15{\%})."
}