Vision-Language multimodal Models (VLMs) offer the possibility for zero-shot
classification in astronomy: i.e. classification via natural language prompts,
with no training. We investigate two models, GPT-4o and LLaVA-NeXT, for
zero-shot classification of low-surface brightness galaxies and artifacts, as
well as morphological classification of galaxies. We show that with natural
language prompts these models achieved significant accuracy (above 80 percent
typically) without additional training/fine tuning. We discuss areas that
require improvement, especially for LLaVA-NeXT, which is an open source model.
Our findings aim to motivate the astronomical community to consider VLMs as a
powerful tool for both research and pedagogy, with the prospect that future
custom-built or fine-tuned models could perform better.

使用自然语言提示，无需额外训练即可实现视觉－语言多模态模型对低表面亮度星系和艺术品的零样本分类以及星系形态分类，在自然语言提示下，这些模型通常能够实现显著的准确度（通常在 80％以上），我们讨论了一些需要改进的领域，特别是 LLaVA-NeXT，这是一个开源模型，我们的研究结果旨在激励天文学界将视觉－语言多模态模型视为研究和教育的强大工具，并希望未来定制或优化的模型能够表现更好。