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arXiv · 2509.21257

Hallucination as an Upper Bound: A New Perspective on Text-to-Image Evaluation

Seyed Amir Kasaei·Mohammad Hossein Rohban
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Abstract

In language and vision-language models, hallucination is broadly understood as content generated from a model's prior knowledge or biases rather than from the given input. While this phenomenon has been studied in those domains, it has not been clearly framed for text-to-image (T2I) generative models. Existing evaluations mainly focus on alignment, checking whether prompt-specified elements appear, but overlook what the model generates beyond the prompt. We argue for defining hallucination in T2I as bias-driven deviations and propose a taxonomy with three categories: attribute, relation, and object hallucinations. This framing introduces an upper bound for evaluation and surfaces hidden biases, providing a foundation for richer assessment of T2I models.

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Seyed Amir Kasaei, Mohammad Hossein Rohban. 2025-09-25. Hallucination as an Upper Bound: A New Perspective on Text-to-Image Evaluation. https://arxiv.org/abs/2509.21257

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