Divergent creativity in humans and large language models

This study systematically benchmarks the semantic divergence of state-of-the-art LLMs against a dataset of 100,000 human participants, using the Divergent Association Task and multiple creative-writing tasks. The findings show that LLMs can surpass average human performance on divergent thinking tasks but remain below the creativity scores of the more creative segment of human participants, revealing a ceiling that current models cannot break through.

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