  Language model (LM) prompting--a popular paradigm for solving NLP tasks--has
been shown to be susceptible to miscalibration and brittleness to slight prompt
variations, caused by its discriminative prompting approach, i.e., predicting
the label given the input. To address these issues, we propose Gen-Z--a
generative prompting framework for zero-shot text classification. GEN-Z is
generative, as it measures the LM likelihood of input text, conditioned on
natural language descriptions of labels. The framework is multivariate, as
label descriptions allow us to seamlessly integrate additional contextual
information about the labels to improve task performance. On various standard
classification benchmarks, with six open-source LM families, we show that
zero-shot classification with simple contextualization of the data source of
the evaluation set consistently outperforms both zero-shot and few-shot
baselines while improving robustness to prompt variations. Further, our
approach enables personalizing classification in a zero-shot manner by
incorporating author, subject, or reader information in the label descriptions.
