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On the proper role of linguistically-oriented deep net analysis in linguistic theorizing

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arxiv 2106.08694 v2 pith:IS7DZU2M submitted 2021-06-16 cs.CL

classification cs.CL
keywords deeplinguisticanalysislinguistically-orientednetworksshouldtheorizingacceptability
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A lively research field has recently emerged that uses experimental methods to probe the linguistic behavior of modern deep networks. While work in this tradition often reports intriguing results about the grammatical skills of deep nets, it is not clear what their implications for linguistic theorizing should be. As a consequence, linguistically-oriented deep net analysis has had very little impact on linguistics at large. In this chapter, I suggest that deep networks should be treated as theories making explicit predictions about the acceptability of linguistic utterances. I argue that, if we overcome some obstacles standing in the way of seriously pursuing this idea, we will gain a powerful new theoretical tool, complementary to mainstream algebraic approaches.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 8 citations worldwide. Full citation record

  1. Language Models Largely Exhibit Human-like Constituent Ordering Preferences

    cs.CL 2025-02 conditional novelty 6.0 of 10

    Across heavy NP shift, dative alternation and multiple PP shift, LLM ordering preferences correlate with human judgments, but particle movement preferences do not.

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