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        <title>Give Me the Facts! A Survey on Factual Knowledge Probing in Pre-trained Language Models</title>
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        <namePart type="given">Paul</namePart>
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            <namePart type="given">Kalika</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.1043</identifier>
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%0 Conference Proceedings
%T Give Me the Facts! A Survey on Factual Knowledge Probing in Pre-trained Language Models
%A Youssef, Paul
%A Koraş, Osman
%A Li, Meijie
%A Schlötterer, Jörg
%A Seifert, Christin
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F youssef-etal-2023-give
%X 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.
%R 10.18653/v1/2023.findings-emnlp.1043
%U https://aclanthology.org/2023.findings-emnlp.1043
%U https://doi.org/10.18653/v1/2023.findings-emnlp.1043
%P 15588-15605

Markdown (Informal)

[Give Me the Facts! A Survey on Factual Knowledge Probing in Pre-trained Language Models](https://aclanthology.org/2023.findings-emnlp.1043) (Youssef et al., Findings 2023)

• Give Me the Facts! A Survey on Factual Knowledge Probing in Pre-trained Language Models (Youssef et al., Findings 2023)

ACL

• Paul Youssef, Osman Koraş, Meijie Li, Jörg Schlötterer, and Christin Seifert. 2023. Give Me the Facts! A Survey on Factual Knowledge Probing in Pre-trained Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 15588–15605, Singapore. Association for Computational Linguistics.