Measuring and Improving Consistency in Pretrained Language Models

Measuring and Improving Consistency in Pretrained Language Models

AbstractConsistency of a model—that is, the invariance of its behavior under meaning-preserving alternations in its input—is a highly desirable property in natural language processing. In this paper we study the question: Are Pretrained Language Models (PLMs) consistent with respect...

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Bibliografiska uppgifter
Huvudupphovsmän: Yanai Elazar, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, Eduard Hovy, Hinrich Schütze, Yoav Goldberg
Materialtyp: Artikel
Språk:English
Publicerad: The MIT Press 2021-01-01
Serie:Transactions of the Association for Computational Linguistics
Länkar:https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00410/107384/Measuring-and-Improving-Consistency-in-Pretrained

Internet

https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00410/107384/Measuring-and-Improving-Consistency-in-Pretrained

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