提出用幻觉上限评估文生图模型，揭示隐藏偏见
Hallucination as an Upper Bound: A New Perspective on Text-to-Image Evaluation
- 将幻觉定义为基于偏见的生成偏差，分三类：属性、关系、对象
- 发现现有评估只看提示对齐，忽略额外生成内容
- 为文生图模型提供更全面的评估框架，适合研究者参考
在语言和视觉-语言模型中，幻觉通常指模型基于自身先验知识或偏见生成的内容，而非来自输入。尽管该现象在这些领域已有研究，但尚未明确应用于文生图（T2I）生成模型。现有评估主要关注对齐性，即检查提示中指定元素是否出现，却忽略了模型在提示之外生成的内容。本文主张将文生图中的幻觉定义为由偏见驱动的偏离，并提出一个包含三类的分类体系：属性、关系和对象幻觉。这一框架为评估设定了上限，揭示了隐藏偏见，为文生图模型的更丰富评估提供了基础。
原文摘要 · Abstract (English)
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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