LogicVal benchmark evaluates constraint satisfaction in Text-to-Image models
Text‑to‑image (t2i) models now produce high‑fidelity images from natural‑language prompts, and researchers have created many benchmarks to test specific abilities. In this paper, the authors propose a new way to evaluate t2i systems by treating the task as a constraint‑satisfaction problem. They build a logical framework that defines objects, attributes, relations, and the constraints that a…
Key points
- LogicVal evaluates t2i models using formal logical constraints on objects, attributes, and relations.
- Best model achieved 98 % accuracy on single‑object constraints but only 53 % on multi‑object relationships.
- On clevr, LogicVal roc‑auc ranged from 0.88 to 0.98, aligning with human judgments.
Using this framework, the team evaluated four leading t2i models across nine classes of constraints, from single‑object attributes to multi‑object relationships involving logical operators such as ∀, ∃, count, and →. The best model scored 98 % on single‑object constraints but only 53 % on complex multi‑object relationships. When multiple constraints were combined, accuracy dropped from 84 % to 20 %. The model also correctly identified only 23 % of unsatisfiable prompts. On the clevr dataset, LogicVal achieved roc‑auc scores between 0.88 and 0.98, showing strong agreement with human judgments.
These results highlight that current t2i models excel at simple attribute binding but struggle with compositional reasoning and logical consistency. The LogicVal benchmark offers a systematic, logic‑based approach to quantify these gaps, guiding future model improvements and safer deployment.
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