[
    {
        "id": "osp-19549",
        "type": "article-journal",
        "title": "NegT2IBench: When Negation Changes the Picture. A Polarity Benchmark for Text-to-Image Models",
        "author": [
            {
                "family": "Elfatairy",
                "given": "Omar"
            },
            {
                "family": "Bravo",
                "given": "Maria A."
            },
            {
                "family": "Bader",
                "given": "Jessica"
            },
            {
                "family": "Akata",
                "given": "Zeynep"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/negt2ibench-when-negation-changes-the-picture-a-polarity-benchmark-for-text-to-image-models",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Text-to-image (T2I) models are judged by benchmarks that measure whether requested content appears, but these benchmarks largely overlook the complementary ability to satisfy negated constraints, for example, generating \"a non-red cup.\" Measuring negation raises challenges not faced by affirmation-based benchmarks and requires careful prompt and evaluation design. We introduce NegT2IBench, a benchmark of 4,800 prompts covering two attribute types and four relation categories. Prompts are organized by polarity: the number of positive statements that must hold and negated statements that must not, each ranging from 0 to 2. Varying the two independently separates the effect of negation from the effect of prompt complexity. Our detector-based scoring is reproducible, auditable, and pinpoints which requirement failed. On 600 images with three-annotator labels, it agrees with humans as closely as vision-language judges up to 30x larger, while using only a fraction of their GPU memory. Across eleven T2I models and 211,200 images, nine score lower on a single negated statement than on a single positive one. Per-statement scoring reveals that the loss is largest for color and near zero for proximity, and that 41.5% of failed statements render exactly what the prompt forbids. Rendering what a prompt asks for and withholding what it forbids are distinct capabilities that an aggregate compositional score cannot distinguish. NegT2IBench measures the latter directly, providing a controlled testbed for diagnosing negation failures and developing methods to overcome them."
    }
]