<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="fang-2023-automatic">
    <titleInfo>
        <title>Automatic Detection of Machine-Generated Text Using Pre-Trained Language Models</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yunhao</namePart>
        <namePart type="family">Fang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Smaranda</namePart>
            <namePart type="family">Muresan</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Vivian</namePart>
            <namePart type="family">Chen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kennington</namePart>
            <namePart type="family">Casey</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Vandyke</namePart>
            <namePart type="family">David</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Dethlefs</namePart>
            <namePart type="family">Nina</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Inoue</namePart>
            <namePart type="family">Koji</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ekstedt</namePart>
            <namePart type="family">Erik</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ultes</namePart>
            <namePart type="family">Stefan</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Melbourne, Australia</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this paper, I provide a detailed description of my approach to tackling the ALTA 2023 shared task whose objective is to build an automatic detection system to distinguish between humanauthored text and text generated from Large Language Models. By leveraging several pretrained language models through model finetuning as well as the multi-model ensemble, the system managed to achieve second place on the test set leaderboard in the competition.</abstract>
    <identifier type="citekey">fang-2023-automatic</identifier>
    <location>
        <url>https://aclanthology.org/2023.alta-1.19</url>
    </location>
    <part>
        <date>2023-11</date>
        <extent unit="page">
            <start>159</start>
            <end>163</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Automatic Detection of Machine-Generated Text Using Pre-Trained Language Models
%A Fang, Yunhao
%Y Muresan, Smaranda
%Y Chen, Vivian
%Y Casey, Kennington
%Y David, Vandyke
%Y Nina, Dethlefs
%Y Koji, Inoue
%Y Erik, Ekstedt
%Y Stefan, Ultes
%S Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association
%D 2023
%8 November
%I Association for Computational Linguistics
%C Melbourne, Australia
%F fang-2023-automatic
%X In this paper, I provide a detailed description of my approach to tackling the ALTA 2023 shared task whose objective is to build an automatic detection system to distinguish between humanauthored text and text generated from Large Language Models. By leveraging several pretrained language models through model finetuning as well as the multi-model ensemble, the system managed to achieve second place on the test set leaderboard in the competition.
%U https://aclanthology.org/2023.alta-1.19
%P 159-163

Markdown (Informal)

[Automatic Detection of Machine-Generated Text Using Pre-Trained Language Models](https://aclanthology.org/2023.alta-1.19) (Fang, ALTA 2023)

• Automatic Detection of Machine-Generated Text Using Pre-Trained Language Models (Fang, ALTA 2023)

ACL

• Yunhao Fang. 2023. Automatic Detection of Machine-Generated Text Using Pre-Trained Language Models. In Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association, pages 159–163, Melbourne, Australia. Association for Computational Linguistics.