Context Limitations Make Neural Language Models More Human-Like

Ana Brassard; Kentaro Inui; Tatsuki Kuribayashi; Yohei Oseki

Context Limitations Make Neural Language Models More Human-Like

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arxiv 2205.11463 v2 pith:VNP5CNYA submitted 2022-05-23 cs.CL

Tatsuki Kuribayashi , Yohei Oseki , Ana Brassard , Kentaro Inui This is my paper

classification cs.CL

keywords contextaccesscognitivehumanslanguagemodelsneuralreading

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Language models (LMs) have been used in cognitive modeling as well as engineering studies -- they compute information-theoretic complexity metrics that simulate humans' cognitive load during reading. This study highlights a limitation of modern neural LMs as the model of choice for this purpose: there is a discrepancy between their context access capacities and that of humans. Our results showed that constraining the LMs' context access improved their simulation of human reading behavior. We also showed that LM-human gaps in context access were associated with specific syntactic constructions; incorporating syntactic biases into LMs' context access might enhance their cognitive plausibility.

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