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SPE: Symmetrical Prompt Enhancement for Fact Probing

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arxiv 2211.07078 v1 pith:TJMXFZX6 submitted 2022-11-14 cs.CL cs.AIcs.LG

SPE: Symmetrical Prompt Enhancement for Fact Probing

Yiyuan Li , Tong Che , Yezhen Wang , Zhengbao Jiang , Caiming Xiong , Snigdha Chaturvedi This is my paper
classification cs.CL cs.AIcs.LG
keywords probingfactualplmspredictionsymmetricalcontinuousenhancementknowledge
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Pretrained language models (PLMs) have been shown to accumulate factual knowledge during pretrainingng (Petroni et al., 2019). Recent works probe PLMs for the extent of this knowledge through prompts either in discrete or continuous forms. However, these methods do not consider symmetry of the task: object prediction and subject prediction. In this work, we propose Symmetrical Prompt Enhancement (SPE), a continuous prompt-based method for factual probing in PLMs that leverages the symmetry of the task by constructing symmetrical prompts for subject and object prediction. Our results on a popular factual probing dataset, LAMA, show significant improvement of SPE over previous probing methods.

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