@inproceedings{nakajima-etal-2026-beyond,
    title = "Beyond Divergent Creativity: A Human-Based Evaluation of Creativity in Large Language Models",
    author = "Nakajima, Kumiko  and
      Zuiderveld, Jan  and
      Pezzelle, Sandro",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.138/",
    pages = "2639--2660",
    ISBN = "979-8-89176-386-9",
    abstract = "Large language models (LLMs) are increasingly used in verbal creative tasks. However, previous assessments of the creative capabilities of LLMs remain weakly grounded in human creativity theory and are thus hard to interpret. The widely used Divergent Association Task (DAT) focuses on novelty, ignoring appropriateness, a core component of creativity. We evaluate a range of state-of-the-art LLMs on DAT and show that their scores on the task are lower than those of two baselines that do not possess any creative abilities, undermining its validity for model evaluation. Grounded in human creativity theory, which defines creativity as the combination of novelty and appropriateness, we introduce the Conditional Divergent Association Task (CDAT). CDAT evaluates novelty conditional on contextual appropriateness, separatingnoise from creativity better than DAT, while remaining simple and objective. Under CDAT, smaller model families often show the most creativity, whereas advanced families favor appropriateness at lower novelty. We hypothesize that training and alignment likely shift models along this frontier, making outputsmore appropriate but less creative. We release the dataset and code."
}