Vision-and-Language models such as CLIP have demonstrated remarkable
effectiveness across a wide range of tasks. However, these models are typically
trained on web-scale data, which can introduce inappropriate content and lead
to the development of unsafe and biased behavior. This, in turn, hampers their
applicability in sensitive and trustworthy contexts and could raise significant
concern in their adoption. To overcome these limitations, we introduce a
methodology to make Vision-and-Language models safer by removing their
sensitivity to not-safe-for-work concepts. We show how this can be done by
distilling from a large language model which converts between safe and unsafe
sentences and which is fine-tuned starting from just 100 manually-curated
pairs. We conduct extensive experiments on the resulting embedding space for
both retrieval and text-to-image generation, where we show that our model can
also be properly employed with pre-trained image generators. Our source code
and trained models are available at: this https URL

通过从大型语言模型中提取训练数据并进行微调，我们介绍了一种使视觉与语言模型对不适宜的内容不敏感的方法，并展示了它在文本 - 图像检索和生成中的有效性。