כתבה
arXiv cs.AI ·
NegT2IBench: כשהשלילה משנה את התמונה. מבחן פולריות למודלי טקסט-לתמונה
NegT2IBench: When Negation Changes the Picture. A Polarity Benchmark for Text-to-Image Models
מבחן למודלי טקסט-לתמונה למדידת יכולת השלילה
תקציר מקורי באנגליתarXiv:2610.03084v1 Announce Type: cross 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 s
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arxiv.org
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