Creativity

When there is no correct answer, what remains to measure? Taste sounds private and slippery, but it leaves observable traces: repeated choices, confidence, sensitivity to framing, and disagreement between judges. The repository contains an experiment across art, poetry, music, design, and prose that turns those traces into data. The claim under investigation is deliberately limited: Language models produce stable, model specific aesthetic preference profiles, even when no option is objectively correct....

Here’s a poem. Human or AI? The morning light falls soft on empty chairs, where conversations used to fill the air. Now silence keeps its patient, gentle watch— a house that holds the shape of those who’ve gone. This experiment tests whether LLMs can distinguish AI-generated creative work from human work—and what their detection strategies reveal about what they consider “authentically human.” The Experiment We curated 500 creative works: 250 human-created (published works, attributed artists) 250 AI-generated (GPT-4, Claude, Midjourney prompts) Across 5 domains:...

Do AI systems have genuine aesthetic preferences, or are they just pattern-matching to training data? This experiment probes the aesthetic “taste” of different LLMs across art, poetry, music, design, and writing—testing whether they exhibit consistent, model-specific preferences. The Experiment We presented 15 aesthetic comparison pairs across 5 domains: Visual Art: Abstract vs. representational, minimal vs. complex Poetry: Rhyming vs. free verse, dense vs. sparse Music: Harmonic vs. dissonant, simple vs. complex Design: Ornate vs....