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Measuring and Improving Consistency in Pretrained Language Models

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arxiv 2102.01017 v2 pith:5BW37UTG submitted 2021-02-01 cs.CL

Yanai Elazar , Nora Kassner , Shauli Ravfogel , Abhilasha Ravichander , Eduard Hovy , Hinrich Schütze , Yoav Goldberg This is my paper · ORCID
classification cs.CL
keywords consistencylanguageplmsimprovingknowledgemodelmodelsparaphrases
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Consistency 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 to factual knowledge? To this end, we create ParaRel, a high-quality resource of cloze-style query English paraphrases. It contains a total of 328 paraphrases for 38 relations. Using ParaRel, we show that the consistency of all PLMs we experiment with is poor -- though with high variance between relations. Our analysis of the representational spaces of PLMs suggests that they have a poor structure and are currently not suitable for representing knowledge robustly. Finally, we propose a method for improving model consistency and experimentally demonstrate its effectiveness.

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