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To mitigate the impact of the scarcity of labelled data on fact-checkingsystems, we focus on few-shot claim verification. Despite recent work onfew-shot classification by proposing advanced language models, there is adearth of research in data annotation prioritisation that improves theselection of the few shots to be labelled for optimal model performance. Wepropose Active PETs, a novel weighted approach that utilises an ensemble ofPattern Exploiting Training (PET) models based on various language models, toactively select unlabelled data as candidates for annotation. Using Active PETsfor few-shot data selection shows consistent improvement over the baselinemethods, on two technical fact-checking datasets and using six differentpretrained language models. We show further improvement with Active PETs-o,which further integrates an oversampling strategy. Our approach enableseffective selection of instances to be labelled where unlabelled data isabundant but resources for labelling are limited, leading to consistentlyimproved few-shot claim verification performance. Our code is available.

Active PETs: Active Data Annotation Prioritisation for Few-Shot Claim Verification with Pattern Exploiting Training