@inproceedings{kuribayashi-etal-2026-dual,
title = "Dual Alignment Between Language Model Layers and Human Sentence Processing",
author = "Kuribayashi, Tatsuki and
Warstadt, Alex and
Oseki, Yohei and
Wilcox, Ethan Gotlieb",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.2143/",
pages = "46207--46223",
ISBN = "979-8-89176-390-6",
abstract = "A recent study{~}(CITATION) has shown that human sentence processing behavior, typically measured on syntactically unchallenging constructions, can be effectively modeled using surprisal from early layers of large language models (LLMs). This raises the question of whether such advantages of internal layers extend to more syntactically challenging constructions, where surprisal has been reported to underestimate human cognitive effort.In this paper, we begin by exploring internal layers that better estimate human cognitive effort observed in syntactic ambiguity processing in English.Our experiments show that, in contrast to naturalistic reading, later layers better estimate such a cognitive effort, but still underestimate the human data. This \textit{dual alignment} sheds light on different modes of sentence processing in humans and LMs: naturalistic reading employs a somewhat weak prediction akin to earlier layers of LMs, while syntactically challenging processing requires more fully-contextualized representations, better modeled by later layers of LMs.Motivated by these findings, we also explore several probability-update measures using shallow and deep layers of LMs, showing a complementary advantage to single-layer{'}s surprisal in reading time modeling."
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%0 Conference Proceedings
%T Dual Alignment Between Language Model Layers and Human Sentence Processing
%A Kuribayashi, Tatsuki
%A Warstadt, Alex
%A Oseki, Yohei
%A Wilcox, Ethan Gotlieb
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F kuribayashi-etal-2026-dual
%X A recent study (CITATION) has shown that human sentence processing behavior, typically measured on syntactically unchallenging constructions, can be effectively modeled using surprisal from early layers of large language models (LLMs). This raises the question of whether such advantages of internal layers extend to more syntactically challenging constructions, where surprisal has been reported to underestimate human cognitive effort.In this paper, we begin by exploring internal layers that better estimate human cognitive effort observed in syntactic ambiguity processing in English.Our experiments show that, in contrast to naturalistic reading, later layers better estimate such a cognitive effort, but still underestimate the human data. This dual alignment sheds light on different modes of sentence processing in humans and LMs: naturalistic reading employs a somewhat weak prediction akin to earlier layers of LMs, while syntactically challenging processing requires more fully-contextualized representations, better modeled by later layers of LMs.Motivated by these findings, we also explore several probability-update measures using shallow and deep layers of LMs, showing a complementary advantage to single-layer’s surprisal in reading time modeling.
%U https://aclanthology.org/2026.acl-long.2143/
%P 46207-46223
Markdown (Informal)
[Dual Alignment Between Language Model Layers and Human Sentence Processing](https://aclanthology.org/2026.acl-long.2143/) (Kuribayashi et al., ACL 2026)
ACL
- Tatsuki Kuribayashi, Alex Warstadt, Yohei Oseki, and Ethan Gotlieb Wilcox. 2026. Dual Alignment Between Language Model Layers and Human Sentence Processing. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 46207–46223, San Diego, California, United States. Association for Computational Linguistics.