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Language Models as Fact Checkers?
2020
Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER)
unpublished
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
doi:10.18653/v1/2020.fever-1.5
fatcat:iia7vzmz5vhmjikwuylggxlrdy