@inproceedings{kuribayashi-etal-2022-context,
title = "Context Limitations Make Neural Language Models More Human-Like",
author = "Kuribayashi, Tatsuki and
Oseki, Yohei and
Brassard, Ana and
Inui, Kentaro",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.712",
doi = "10.18653/v1/2022.emnlp-main.712",
pages = "10421--10436",
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.",
}
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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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%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)
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.