arXiv Computation and Language By Manar Ali, Lisa Gottschalk, Simeon Junker, Lisa Hudalla, Alon Fishman, Joana Cholin, Stefan Kopp, Hendrik Buschmeier, Sina Zarrie{\ss}

Testing Associative Creativity in Humans and Multimodal LLMs with Difficult-to-Name Visual Stimuli

Read the original on arXiv Computation and Language →

The paper examines how humans and multimodal large language models (MLLMs) generate associative descriptions for abstract visual stimuli that become harder to name. Using two datasets—SilCC, which increases difficulty by combining abstract shapes, and PixCC, which adds visual noise—the study measures originality and effectiveness of the generated descriptions. Findings show that humans produce more diverse, semantically distant, and novel descriptions than MLLMs, and that as difficulty rises, human effectiveness drops in SilCC while they shift toward low-level visual descriptions in PixCC.

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