Verifiable, Articulable, and Tacit Components of Preference
Read the original on arXiv AI →The paper introduces CreativePreferences, a large dataset of 2.8 million texts labeled by 317 million human preference judgments across seven creative domains, and explores how preference components that are tacit, articulable, and verifiable differ. By modeling these labels with executable programs, rubric banks, and densely trained models, the authors identify robust articulability and verifiability gaps—especially in domains like peer review and creative writing—and propose a novel measurement approach to estimate these gaps. The study demonstrates that fully articulated models can diverge from human preferences and that articulating preferences may shift them away from tacit dimensions, offering recommendations on task prompting and learning strategies. "whyItMatters":"The findings highlight that current AI preference modeling may miss crucial tacit aspects, potentially leading to misaligned or suboptimal outcomes in creative and evaluative tasks."
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