Recent work has suggested that language models (LMs) store both common-sense
and factual knowledge learned from pre-training data. In this paper, we
leverage this implicit knowledge to create an effective end-to-end fact checker
using a solely a language model, without any external knowledge or explicit
retrieval components. While previous work on extracting knowledge from LMs have
focused on the task of open-domain question answering, to the best of our
knowledge, this is the first work to examine the use of language models as fact
checkers. In a closed-book setting, we show that our zero-shot LM approach
outperforms a random baseline on the standard FEVER task, and that our
fine-tuned LM compares favorably with standard baselines. Though we do not
ultimately outperform methods which use explicit knowledge bases, we believe
our exploration shows that this method is viable and has much room for
exploration.

本文探索了利用语言模型自带的知识来创建基于 LM 的事实检查器的方法，并在闭卷条件下展示了我们的零 - shot LM 方法在标准 FEVER 任务上优于随机基准线，而我们的微调 LM 与标准基准线相比表现出色。虽然我们最终没有超越使用显式知识库的方法，但我们相信我们的探索显示出这种方法是可行的，并且有很大的发展空间。