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

Submission Type: Regular Long Paper

Submission Track: Language Modeling and Analysis of Language Models

Keywords: factual knowledge probing

TL;DR: We survey publications that probe language models for factual knowledge.

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.

Submission Number: 2635