Give Me the Facts! A Survey on Factual Knowledge Probing in Pre-trained Language Models
Paul Youssef, Osman Alperen Koraş, Meijie Li, Jörg Schlötterer, Christin Seifert
Abstract
Pre-trained Language Models (PLMs) are trained on vast unlabeled data, rich in world knowledge. This fact has sparked the interest of the community in quantifying the amount of factual knowledge present in PLMs, as this explains their performance on downstream tasks, and potentially justifies their use as knowledge bases. In this work, we survey methods and datasets that are used to probe PLMs for factual knowledge. Our contributions are: (1) We propose a categorization scheme for factual probing methods that is based on how their inputs, outputs and the probed PLMs are adapted; (2) We provide an overview of the datasets used for factual probing; (3) We synthesize insights about knowledge retention and prompt optimization in PLMs, analyze obstacles to adopting PLMs as knowledge bases and outline directions for future work.
BibTeX
@inproceedings{
youssef2023give,
title={Give Me the Facts! A Survey on Factual Knowledge Probing in Pre-trained Language Models},
author={Paul Youssef and Osman Alperen Kora{\c{s}} and Meijie Li and J{\"o}rg Schl{\"o}tterer and Christin Seifert},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=0kseDcA5Nm}
}