Hallucination as an Upper Bound: A New Perspective on Text-to-Image Evaluation
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918191535489024 |
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| author | Kasaei, Seyed Amir Rohban, Mohammad Hossein |
| author_facet | Kasaei, Seyed Amir Rohban, Mohammad Hossein |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21257 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hallucination as an Upper Bound: A New Perspective on Text-to-Image Evaluation Kasaei, Seyed Amir Rohban, Mohammad Hossein Computer Vision and Pattern Recognition Computation and Language 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. |
| title | Hallucination as an Upper Bound: A New Perspective on Text-to-Image Evaluation |
| topic | Computer Vision and Pattern Recognition Computation and Language |
| url | https://arxiv.org/abs/2509.21257 |