  Weakly-supervised learning (WSL) has shown promising results in addressing
label scarcity on many NLP tasks, but manually designing a comprehensive,
high-quality labeling rule set is tedious and difficult. We study interactive
weakly-supervised learning -- the problem of iteratively and automatically
discovering novel labeling rules from data to improve the WSL model. Our
proposed model, named PRBoost, achieves this goal via iterative prompt-based
rule discovery and model boosting. It uses boosting to identify large-error
instances and then discovers candidate rules from them by prompting pre-trained
LMs with rule templates. The candidate rules are judged by human experts, and
the accepted rules are used to generate complementary weak labels and
strengthen the current model. Experiments on four tasks show PRBoost
outperforms state-of-the-art WSL baselines up to 7.1% and bridges the gaps with
fully supervised models. Our Implementation is available at
\url{https://github.com/rz-zhang/PRBoost}.
