---
title: "Beyond Divergent Creativity — paper by Nakajima et al. (2026)"
description: "The paper argues that the Divergent Association Task (DAT), a popular test of verbal creativity, is a poor yardstick for LLMs. 51 references."
authors: ["Kumiko Nakajima", "Jan Zuiderveld", "Sandro Pezzelle"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: EACL 2026"
source: "https://aclanthology.org/2026.findings-eacl.138/"
doi: "10.18653/v1/2026.findings-eacl.138"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/nakajima-2026-beyond-divergent-creativity-human-based/"
---
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# Beyond Divergent Creativity: A Human-Based Evaluation of Creativity in Large Language Models

**Authors:** Kumiko Nakajima, Jan Zuiderveld and [Sandro Pezzelle](https://md.research.elamur.ai/free/en/authors/sandro-pezzelle/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: EACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-eacl.138/) · [PDF](https://aclanthology.org/2026.findings-eacl.138.pdf) · [DOI 10.18653/v1/2026.findings-eacl.138](https://doi.org/10.18653/v1/2026.findings-eacl.138) · [OpenAlex](https://openalex.org/W7140121687)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
The paper argues that the Divergent Association Task (DAT), a popular test of verbal creativity, is a poor yardstick for LLMs. DAT asks for 10 maximally unrelated nouns and rewards only semantic distance, so it captures novelty while ignoring whether the output fits any context. The authors tested a range of state-of-the-art LLMs at several sampling temperatures against two non-creative baselines: random WordNet words and a strategy that games the scoring. Both baselines outscored the models, which undermines DAT as a measure of model creativity.

Drawing on psychological accounts that treat creativity as novelty combined with appropriateness, they propose the Conditional Divergent Association Task (CDAT). In CDAT, models produce 10 words that are diverse but still related to a given cue word. Outputs are scored on two axes, and a Pareto-front metric summarises the balance between them. Under CDAT, smaller model families tend to come out as the most creative. Larger, more aligned families trade novelty for appropriateness. The authors suggest, without claiming causation, that training and alignment push models toward safer outputs.

## Key points
- **DAT scoring:** only the first seven valid nouns count, with validity checked via NLTK lemmatisation, POS tagging and WordNet noun synsets. Pairwise novelty is 100·(1−cosine), ranging from 0 to 200.
- **Baselines:** a task-agnostic random WordNet sampler and a task-aware "cheating" baseline both beat SotA LLMs on DAT.
- **CDAT scoring:**
  - Novelty is the mean pairwise distance among the generated words.
  - Appropriateness is the distance from the cue word.
  - An appropriateness gate compares each model against the Random baseline using Welch's t-test (α=0.001).
- **Embeddings:** SBERT is the main representation. A sensitivity analysis covers GloVe, fastText and BERT.
- **Human comparison and release:** the human reference comes from 99 participants. The dataset is released on OSF and the code on GitHub.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [University of Amsterdam](https://md.research.elamur.ai/free/en/universities/university-of-amsterdam/) — University, Amsterdam, Netherlands · 151 works · 174,506 citations

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## Cite

Cite as: Kumiko Nakajima, Jan Zuiderveld and Sandro Pezzelle. 2026. Beyond Divergent Creativity: A Human-Based Evaluation of Creativity in Large Language Models. Findings of the Association for Computational Linguistics: EACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{nakajima2026beyond,
  title = {{Beyond Divergent Creativity: A Human-Based Evaluation of Creativity in Large Language Models}},
  author = {Kumiko Nakajima, Jan Zuiderveld and Sandro Pezzelle},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-eacl.138},
  url = {https://doi.org/10.18653/v1/2026.findings-eacl.138}
}
```

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*Updated 1 November 2026*