  Few-shot or zero-shot fact verification only relies on a few or no labeled
training examples. In this paper, we propose a novel method called ProToCo, to
\underline{Pro}mpt pre-trained language models (PLMs) \underline{To} be
\underline{Co}nsistent, for improving the factuality assessment capability of
PLMs in the few-shot and zero-shot settings. Given a claim-evidence pair,
ProToCo generates multiple variants of the claim with different relations and
frames a simple consistency mechanism as constraints for making compatible
predictions across these variants. We update PLMs by using parameter-efficient
fine-tuning (PEFT), leading to more accurate predictions in few-shot and
zero-shot fact verification tasks. Our experiments on three public verification
datasets show that ProToCo significantly outperforms state-of-the-art few-shot
fact verification baselines. With a small number of unlabeled instances,
ProToCo also outperforms the strong zero-shot learner T0 on zero-shot
verification. Compared to large PLMs using in-context learning (ICL) method,
ProToCo outperforms OPT-30B and the Self-Consistency-enabled OPT-6.7B model in
both few- and zero-shot settings.
