  Learning Using Privileged Information is a particular type of knowledge
distillation where the teacher model benefits from an additional data
representation during training, called privileged information, improving the
student model, which does not see the extra representation. However, privileged
information is rarely available in practice. To this end, we propose a text
classification framework that harnesses text-to-image diffusion models to
generate artificial privileged information. The generated images and the
original text samples are further used to train multimodal teacher models based
on state-of-the-art transformer-based architectures. Finally, the knowledge
from multimodal teachers is distilled into a text-based (unimodal) student.
Hence, by employing a generative model to produce synthetic data as privileged
information, we guide the training of the student model. Our framework, called
Learning Using Generated Privileged Information (LUGPI), yields noticeable
performance gains on four text classification data sets, demonstrating its
potential in text classification without any additional cost during inference.
