  To mitigate the impact of the scarcity of labelled data on fact-checking
systems, we focus on few-shot claim verification. Despite recent work on
few-shot classification by proposing advanced language models, there is a
dearth of research in data annotation prioritisation that improves the
selection of the few shots to be labelled for optimal model performance. We
propose Active PETs, a novel weighted approach that utilises an ensemble of
Pattern Exploiting Training (PET) models based on various language models, to
actively select unlabelled data as candidates for annotation. Using Active PETs
for few-shot data selection shows consistent improvement over the baseline
methods, on two technical fact-checking datasets and using six different
pretrained language models. We show further improvement with Active PETs-o,
which further integrates an oversampling strategy. Our approach enables
effective selection of instances to be labelled where unlabelled data is
abundant but resources for labelling are limited, leading to consistently
improved few-shot claim verification performance. Our code is available.
