While human speakers use a variety of different expressions when describing
the same object in an image, giving rise to a distribution of plausible labels
driven by pragmatic constraints, the extent to which current Vision \& Language
Large Language Models (VLLMs) can mimic this crucial feature of language use is
an open question. This applies to common, everyday objects, but it is
particularly interesting for uncommon or novel objects for which a category
label may be lacking or fuzzy. Furthermore, humans show clear production
preferences for highly context-sensitive expressions, such as the quantifiers
`few' or `most'. In our work, we evaluate VLLMs (FROMAGe, BLIP-2, LLaVA) on
three categories (nouns, attributes, and quantifiers) where humans show great
subjective variability concerning the distribution over plausible labels, using
datasets and resources mostly under-explored in previous work. Our results
reveal mixed evidence on the ability of VLLMs to capture human naming
preferences, with all models failing in tasks that require high-level reasoning
such as assigning quantifiers.

当前的视觉与语言大型语言模型（VLLMs）在捕捉人类命名偏好方面的能力存在问 题，尤其在需要高级推理能力的任务中，例如指定量词。