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        <title>Context Limitations Make Neural Language Models More Human-Like</title>
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        <namePart type="given">Yohei</namePart>
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            <namePart type="given">Yoav</namePart>
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            <namePart type="given">Yue</namePart>
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    <abstract>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.</abstract>
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    <identifier type="doi">10.18653/v1/2022.emnlp-main.712</identifier>
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%0 Conference Proceedings
%T Context Limitations Make Neural Language Models More Human-Like
%A Kuribayashi, Tatsuki
%A Oseki, Yohei
%A Brassard, Ana
%A Inui, Kentaro
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F kuribayashi-etal-2022-context
%X 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.
%R 10.18653/v1/2022.emnlp-main.712
%U https://aclanthology.org/2022.emnlp-main.712
%U https://doi.org/10.18653/v1/2022.emnlp-main.712
%P 10421-10436

Markdown (Informal)

[Context Limitations Make Neural Language Models More Human-Like](https://aclanthology.org/2022.emnlp-main.712) (Kuribayashi et al., EMNLP 2022)

• Context Limitations Make Neural Language Models More Human-Like (Kuribayashi et al., EMNLP 2022)

ACL

• Tatsuki Kuribayashi, Yohei Oseki, Ana Brassard, and Kentaro Inui. 2022. Context Limitations Make Neural Language Models More Human-Like. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 10421–10436, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.