TASTE: A Designer-Annotated Multi-Dimensional Preference Dataset for AI-Generated Graphic Design
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910239799902208 |
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| author | Zhu, Haonan Hirsch, Elad Minetti, Alexandria Nulty, Allison Mehta, Purvanshi |
| author_facet | Zhu, Haonan Hirsch, Elad Minetti, Alexandria Nulty, Allison Mehta, Purvanshi |
| contents | Text-to-image models produce graphic design at production scale, but their supervision comes from photo-style preference data with a single overall verdict per comparison. Designers evaluate along several distinct axes, including typography, visual hierarchy, color harmony, layout, and brief fidelity, and a single label collapses them. We release TASTE (Typography, Aesthetics, Spatial, Tone, Etc.): ten professional designers ranked outputs from four current text-to-image models on nine criteria across two disjoint cohorts, yielding 1,600 ratings per criterion plus per-image hallucination flags on the holistic-preference cohorts. We pair the dataset with three contributions. First, a criterion-agnostic signal test framework, using Kendall's tau, majority probability, and Condorcet cycles against exact iid-uniform nulls at p = 4 and R = 5, places designer agreement on graphic design between food and movie preferences and photo-style image quality, with every TASTE criterion rejecting the random-rater null. Second, no pre-trained system in our benchmark, including six open-weight VLM judges from 3B to 33B parameters and three dedicated T2I scorers, HPSv2.1, PickScore-v1, and LAION-Aesthetic-V2, exceeds 0.55 macro agreement with the 5-designer majority; VLM judges trade off position bias against content sensitivity, so scaling moves along this frontier without improving accuracy. Third, a small pairwise-difference head trained on TASTE reaches 0.611, closing roughly half the gap to the 0.741 single-rater ceiling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_20731 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TASTE: A Designer-Annotated Multi-Dimensional Preference Dataset for AI-Generated Graphic Design Zhu, Haonan Hirsch, Elad Minetti, Alexandria Nulty, Allison Mehta, Purvanshi Computer Vision and Pattern Recognition Artificial Intelligence Applications Text-to-image models produce graphic design at production scale, but their supervision comes from photo-style preference data with a single overall verdict per comparison. Designers evaluate along several distinct axes, including typography, visual hierarchy, color harmony, layout, and brief fidelity, and a single label collapses them. We release TASTE (Typography, Aesthetics, Spatial, Tone, Etc.): ten professional designers ranked outputs from four current text-to-image models on nine criteria across two disjoint cohorts, yielding 1,600 ratings per criterion plus per-image hallucination flags on the holistic-preference cohorts. We pair the dataset with three contributions. First, a criterion-agnostic signal test framework, using Kendall's tau, majority probability, and Condorcet cycles against exact iid-uniform nulls at p = 4 and R = 5, places designer agreement on graphic design between food and movie preferences and photo-style image quality, with every TASTE criterion rejecting the random-rater null. Second, no pre-trained system in our benchmark, including six open-weight VLM judges from 3B to 33B parameters and three dedicated T2I scorers, HPSv2.1, PickScore-v1, and LAION-Aesthetic-V2, exceeds 0.55 macro agreement with the 5-designer majority; VLM judges trade off position bias against content sensitivity, so scaling moves along this frontier without improving accuracy. Third, a small pairwise-difference head trained on TASTE reaches 0.611, closing roughly half the gap to the 0.741 single-rater ceiling. |
| title | TASTE: A Designer-Annotated Multi-Dimensional Preference Dataset for AI-Generated Graphic Design |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Applications |
| url | https://arxiv.org/abs/2605.20731 |