<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="jumelet-etal-2024-language">
    <titleInfo>
        <title>Do Language Models Exhibit Human-like Structural Priming Effects?</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Jaap</namePart>
        <namePart type="family">Jumelet</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Willem</namePart>
        <namePart type="family">Zuidema</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Arabella</namePart>
        <namePart type="family">Sinclair</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics ACL 2024</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Lun-Wei</namePart>
            <namePart type="family">Ku</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Andre</namePart>
            <namePart type="family">Martins</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Vivek</namePart>
            <namePart type="family">Srikumar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Bangkok, Thailand and virtual meeting</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We explore which linguistic factors—at the sentence and token level—play an important role in influencing language model predictions, and investigate whether these are reflective of results found in humans and human corpora (Gries and Kootstra, 2017). We make use of the structural priming paradigm—where recent exposure to a structure facilitates processing of the same structure—to investigate where priming effects manifest, and what factors predict them. We find these effects can be explained via the inverse frequency effect found in human priming, where rarer elements within a prime increase priming effects, as well as lexical dependence between prime and target. Our results provide an important piece in the puzzle of understanding how properties within their context affect structural prediction in language models.</abstract>
    <identifier type="citekey">jumelet-etal-2024-language</identifier>
    <location>
        <url>https://aclanthology.org/2024.findings-acl.877</url>
    </location>
    <part>
        <date>2024-08</date>
        <extent unit="page">
            <start>14727</start>
            <end>14742</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Do Language Models Exhibit Human-like Structural Priming Effects?
%A Jumelet, Jaap
%A Zuidema, Willem
%A Sinclair, Arabella
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F jumelet-etal-2024-language
%X We explore which linguistic factors—at the sentence and token level—play an important role in influencing language model predictions, and investigate whether these are reflective of results found in humans and human corpora (Gries and Kootstra, 2017). We make use of the structural priming paradigm—where recent exposure to a structure facilitates processing of the same structure—to investigate where priming effects manifest, and what factors predict them. We find these effects can be explained via the inverse frequency effect found in human priming, where rarer elements within a prime increase priming effects, as well as lexical dependence between prime and target. Our results provide an important piece in the puzzle of understanding how properties within their context affect structural prediction in language models.
%U https://aclanthology.org/2024.findings-acl.877
%P 14727-14742

Markdown (Informal)

[Do Language Models Exhibit Human-like Structural Priming Effects?](https://aclanthology.org/2024.findings-acl.877) (Jumelet et al., Findings 2024)

• Do Language Models Exhibit Human-like Structural Priming Effects? (Jumelet et al., Findings 2024)

ACL

• Jaap Jumelet, Willem Zuidema, and Arabella Sinclair. 2024. Do Language Models Exhibit Human-like Structural Priming Effects?. In Findings of the Association for Computational Linguistics ACL 2024, pages 14727–14742, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.