  We demonstrate that co-training (Blum & Mitchell, 1998) can improve the
performance of prompt-based learning by using unlabeled data. While prompting
has emerged as a promising paradigm for few-shot and zero-shot learning, it is
often brittle and requires much larger models compared to the standard
supervised setup. We find that co-training makes it possible to improve the
original prompt model and at the same time learn a smaller, downstream
task-specific model. In the case where we only have partial access to a prompt
model (e.g., output probabilities from GPT-3 (Brown et al., 2020)) we learn a
calibration model over the prompt outputs. When we have full access to the
prompt model's gradients but full finetuning remains prohibitively expensive
(e.g., T0 (Sanh et al., 2021)), we learn a set of soft prompt continuous
vectors to iteratively update the prompt model. We find that models trained in
this manner can significantly improve performance on challenging datasets where
there is currently a large gap between prompt-based learning and
fully-supervised models.
