Testing Associative Creativity in Humans and Multimodal LLMs with Difficult-to-Name Visual Stimuli
We investigate associative creativity in descriptions of abstract visual stimuli by humans and multimodal large language models (MLLMs). While conceptual association is central to human communication, it remains unclear how humans and models adapt when visual stimuli become increasingly difficult to map onto familiar semantic concepts. We evaluate several state-of-the-art MLLMs and human participants along two dimensions of creativity, originality and effectiveness, using two datasets with controlled manipulations of difficulty. SilCC varies naming difficulty through progressively more complex combinations of abstract shapes, and PixCC varies difficulty through increasing levels of random...
| Authors | Manar Ali, Lisa Gottschalk, Simeon Junker et al. |
|---|---|
| Primary category | cs.CL |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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