@inproceedings{wang-etal-2025-predict,
title = "What to Predict? Exploring How Sentence Structure Influences Contrast Predictions in Humans and Large Language Models",
author = "Wang, Shuqi and
Duan, Xufeng and
Cai, Zhenguang",
editor = "Kuribayashi, Tatsuki and
Rambelli, Giulia and
Takmaz, Ece and
Wicke, Philipp and
Li, Jixing and
Oh, Byung-Doh",
booktitle = "Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics",
month = may,
year = "2025",
address = "Albuquerque, New Mexico, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.cmcl-1.28/",
doi = "10.18653/v1/2025.cmcl-1.28",
pages = "244--252",
ISBN = "979-8-89176-227-5",
abstract = "This study examines how sentence structure shapes contrast predictions in both humans and large language models (LLMs). Using Mandarin ditransitive constructions {---} double object (DO, ``She gave the girl the candy, but not...'') vs. prepositional object (PO, ``She gave the candy to the girl, but not...'') as a testbed, we employed a sentence continuation task involving three human groups (written, spoken, and prosodically normalized spoken stimuli) and three LLMs (GPT-4o, LLaMA-3, and Qwen-2.5). Two principal findings emerged: (1) Although human participants predominantly focused on the theme (e.g., ``the candy''), contrast predictions were significantly modulated by sentence structure{---}particularly in spoken contexts, where the sentence-final element drew more attention. (2) While LLMs showed a similar reliance on structure, they displayed a larger effect size and more closely resembled human spoken data than written data, indicating a stronger emphasis on linear order in generating contrast predictions. By adopting a unified psycholinguistic paradigm, this study advances our understanding of predictive language processing for both humans and LLMs and informs research on human{--}model alignment in linguistic tasks."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wang-etal-2025-predict">
<titleInfo>
<title>What to Predict? Exploring How Sentence Structure Influences Contrast Predictions in Humans and Large Language Models</title>
</titleInfo>
<name type="personal">
<namePart type="given">Shuqi</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Xufeng</namePart>
<namePart type="family">Duan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhenguang</namePart>
<namePart type="family">Cai</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics</title>
</titleInfo>
<name type="personal">
<namePart type="given">Tatsuki</namePart>
<namePart type="family">Kuribayashi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Giulia</namePart>
<namePart type="family">Rambelli</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ece</namePart>
<namePart type="family">Takmaz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Philipp</namePart>
<namePart type="family">Wicke</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jixing</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Byung-Doh</namePart>
<namePart type="family">Oh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Albuquerque, New Mexico, USA</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-227-5</identifier>
</relatedItem>
<abstract>This study examines how sentence structure shapes contrast predictions in both humans and large language models (LLMs). Using Mandarin ditransitive constructions — double object (DO, “She gave the girl the candy, but not...”) vs. prepositional object (PO, “She gave the candy to the girl, but not...”) as a testbed, we employed a sentence continuation task involving three human groups (written, spoken, and prosodically normalized spoken stimuli) and three LLMs (GPT-4o, LLaMA-3, and Qwen-2.5). Two principal findings emerged: (1) Although human participants predominantly focused on the theme (e.g., “the candy”), contrast predictions were significantly modulated by sentence structure—particularly in spoken contexts, where the sentence-final element drew more attention. (2) While LLMs showed a similar reliance on structure, they displayed a larger effect size and more closely resembled human spoken data than written data, indicating a stronger emphasis on linear order in generating contrast predictions. By adopting a unified psycholinguistic paradigm, this study advances our understanding of predictive language processing for both humans and LLMs and informs research on human–model alignment in linguistic tasks.</abstract>
<identifier type="citekey">wang-etal-2025-predict</identifier>
<identifier type="doi">10.18653/v1/2025.cmcl-1.28</identifier>
<location>
<url>https://aclanthology.org/2025.cmcl-1.28/</url>
</location>
<part>
<date>2025-05</date>
<extent unit="page">
<start>244</start>
<end>252</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T What to Predict? Exploring How Sentence Structure Influences Contrast Predictions in Humans and Large Language Models
%A Wang, Shuqi
%A Duan, Xufeng
%A Cai, Zhenguang
%Y Kuribayashi, Tatsuki
%Y Rambelli, Giulia
%Y Takmaz, Ece
%Y Wicke, Philipp
%Y Li, Jixing
%Y Oh, Byung-Doh
%S Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico, USA
%@ 979-8-89176-227-5
%F wang-etal-2025-predict
%X This study examines how sentence structure shapes contrast predictions in both humans and large language models (LLMs). Using Mandarin ditransitive constructions — double object (DO, “She gave the girl the candy, but not...”) vs. prepositional object (PO, “She gave the candy to the girl, but not...”) as a testbed, we employed a sentence continuation task involving three human groups (written, spoken, and prosodically normalized spoken stimuli) and three LLMs (GPT-4o, LLaMA-3, and Qwen-2.5). Two principal findings emerged: (1) Although human participants predominantly focused on the theme (e.g., “the candy”), contrast predictions were significantly modulated by sentence structure—particularly in spoken contexts, where the sentence-final element drew more attention. (2) While LLMs showed a similar reliance on structure, they displayed a larger effect size and more closely resembled human spoken data than written data, indicating a stronger emphasis on linear order in generating contrast predictions. By adopting a unified psycholinguistic paradigm, this study advances our understanding of predictive language processing for both humans and LLMs and informs research on human–model alignment in linguistic tasks.
%R 10.18653/v1/2025.cmcl-1.28
%U https://aclanthology.org/2025.cmcl-1.28/
%U https://doi.org/10.18653/v1/2025.cmcl-1.28
%P 244-252
Markdown (Informal)
[What to Predict? Exploring How Sentence Structure Influences Contrast Predictions in Humans and Large Language Models](https://aclanthology.org/2025.cmcl-1.28/) (Wang et al., CMCL 2025)
ACL