2022
DOI: 10.31219/osf.io/h39gc
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Incremental Processing of Principle B: Mismatches Between Neural Models and Humans

Abstract: Despite neural language models qualitatively capturing many human linguistic behaviors, recent work has demonstrated that they underestimate the true processing costs of ungrammatical structures. We extend these more fine-grained comparisons between humans and models by investigating the interaction between Principle B and coreference processing. While humans use Principle B to block certain structural positions from affecting their incremental processing, we find that GPT-based language models are influenced … Show more

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Cited by 1 publication
(3 citation statements)
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“…Despite these apparent successes, there are noted limitations. For example, neural models struggle with infrequent constructions (e.g., Wei et al 2021) and in resolving conflicting linguistic processes (e.g., Davis 2022).…”
Section: Neural Models and Acquisitionmentioning
confidence: 99%
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“…Despite these apparent successes, there are noted limitations. For example, neural models struggle with infrequent constructions (e.g., Wei et al 2021) and in resolving conflicting linguistic processes (e.g., Davis 2022).…”
Section: Neural Models and Acquisitionmentioning
confidence: 99%
“…In linking models to acquisition, the claim is that grammatical knowledge follows directly from distributions observed in corpora. At least two, interrelated, components are necessary for this claim to hold: (i) linguistic data should contain the distributions corresponding to grammatical knowledge, and (ii) neural models should be able to recover these facts (see the discussion of superficialism in Davis (2022)).…”
Section: Introductionmentioning
confidence: 99%
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