<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="rieger-etal-2025-petapter">
    <titleInfo>
        <title>PETapter: Leveraging PET-style classification heads for modular few-shot parameter-efficient fine-tuning</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Jonas</namePart>
        <namePart type="family">Rieger</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mattes</namePart>
        <namePart type="family">Ruckdeschel</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Gregor</namePart>
        <namePart type="family">Wiedemann</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2025-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 21st Conference on Natural Language Processing (KONVENS 2025): Long and Short Papers</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Christian</namePart>
            <namePart type="family">Wartena</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ulrich</namePart>
            <namePart type="family">Heid</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>HsH Applied Academics</publisher>
            <place>
                <placeTerm type="text">Hannover, Germany</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <identifier type="citekey">rieger-etal-2025-petapter</identifier>
    <location>
        <url>https://aclanthology.org/2025.konvens-1.29/</url>
    </location>
    <part>
        <date>2025-09</date>
        <extent unit="page">
            <start>333</start>
            <end>348</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T PETapter: Leveraging PET-style classification heads for modular few-shot parameter-efficient fine-tuning
%A Rieger, Jonas
%A Ruckdeschel, Mattes
%A Wiedemann, Gregor
%Y Wartena, Christian
%Y Heid, Ulrich
%S Proceedings of the 21st Conference on Natural Language Processing (KONVENS 2025): Long and Short Papers
%D 2025
%8 September
%I HsH Applied Academics
%C Hannover, Germany
%F rieger-etal-2025-petapter
%U https://aclanthology.org/2025.konvens-1.29/
%P 333-348

Markdown (Informal)

[PETapter: Leveraging PET-style classification heads for modular few-shot parameter-efficient fine-tuning](https://aclanthology.org/2025.konvens-1.29/) (Rieger et al., KONVENS 2025)

• PETapter: Leveraging PET-style classification heads for modular few-shot parameter-efficient fine-tuning (Rieger et al., KONVENS 2025)

ACL

• Jonas Rieger, Mattes Ruckdeschel, and Gregor Wiedemann. 2025. PETapter: Leveraging PET-style classification heads for modular few-shot parameter-efficient fine-tuning. In Proceedings of the 21st Conference on Natural Language Processing (KONVENS 2025): Long and Short Papers, pages 333–348, Hannover, Germany. HsH Applied Academics.