  Prompt-based classifiers are an attractive approach for zero-shot
classification. However, the precise choice of the prompt template and label
words can largely influence performance, with semantically equivalent settings
often showing notable performance difference. This discrepancy can be partly
attributed to word biases, where the classifier may be biased towards classes.
To address this problem, it is possible to optimise classification thresholds
on a labelled data set, however, this mitigates some of the advantages of
prompt-based classifiers. This paper instead approaches this problem by
examining the expected marginal probabilities of the classes. Here,
probabilities are reweighted to have a uniform prior over classes, in an
unsupervised fashion. Further, we draw a theoretical connection between the
class priors and the language models' word prior, and offer the ability to set
a threshold in a zero-resource fashion. We show that matching class priors
correlates strongly with the oracle upper bound performance and demonstrate
large consistent performance gains for prompt settings over a range of NLP
tasks.
