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Structural Persistence in Language Models: Priming as a Window into Abstract Language Representations. (arXiv:2109.14989v2 [cs.CL] UPDATED)
June 30, 2022, 1:12 a.m. | Arabella Sinclair, Jaap Jumelet, Willem Zuidema, Raquel Fernández
cs.CL updates on arXiv.org arxiv.org
We investigate the extent to which modern, neural language models are
susceptible to structural priming, the phenomenon whereby the structure of a
sentence makes the same structure more probable in a follow-up sentence. We
explore how priming can be used to study the potential of these models to learn
abstract structural information, which is a prerequisite for good performance
on tasks that require natural language understanding skills. We introduce a
novel metric and release Prime-LM, a large corpus where we …
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