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New benchmark reveals text-to-image models struggle with negation

By PulseAugur Editorial · [1 sources] ·

A new benchmark, T2I-NSI, has been developed to evaluate how well text-to-image diffusion models handle negation. The benchmark reveals that these models frequently fail to suppress negated concepts, such as not generating a lamp when prompted with "a desk without a lamp." Open-weight models show low negation sensitivity, scoring below 0.038, while models like GPT-Image-1.5 and Gemini-2.5-Flash-Image perform slightly better, around 0.09. A significant finding is the near-absence of inverse interpretation, meaning models rarely understand negation as a directive to include the opposite concept. AI

IMPACT Highlights a critical limitation in current text-to-image models, potentially guiding future research towards more robust negation handling.

RANK_REASON The cluster contains a research paper introducing a new benchmark for evaluating AI model capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark reveals text-to-image models struggle with negation

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The cluster contains a research paper introducing a new benchmark for evaluating AI model capabilities. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Akansha Gautam, Aparna Bharati, Mayank Vatsa, Richa Singh ·

    T2I Models Rarely Invert Negation but Often Fail to Suppress Negated Concepts: Benchmarking Negation Suppression in Text-to-Image Generation

    arXiv:2611.09304v1 Announce Type: new Abstract: Text-to-image diffusion models often fail to suppress explicitly negated concepts, such as a lamp for "a desk without a lamp." We introduce T2I-NSI, a benchmark and metric for measuring negation sensitivity in text-to-image generati…

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