<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="qian-guo-2024-enhancing">
    <titleInfo>
        <title>Enhancing Consistency Through Prompt-Tuning for Style Guide Adaptation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Ming</namePart>
        <namePart type="family">Qian</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Zidian</namePart>
        <namePart type="family">Guo</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 16th Conference of the Association for Machine Translation in the Americas (Volume 2: Presentations)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Marianna</namePart>
            <namePart type="family">Martindale</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Janice</namePart>
            <namePart type="family">Campbell</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Konstantin</namePart>
            <namePart type="family">Savenkov</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Shivali</namePart>
            <namePart type="family">Goel</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Machine Translation in the Americas</publisher>
            <place>
                <placeTerm type="text">Chicago, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>This presentation explores the use of Prompt-Tuning (PT) to improve brand and language consistency in localization by teaching Large Language Models (LLMs) to develop and apply style guides from minimal examples. PT allows for the automatic enforcement of style guides for specific projects, potentially enhancing translation quality across varied tasks. Our approach involves defining key style guide components such as domain, audience, and formatting standards for acronyms, dates, and measurements, and creating prompts that instruct LLMs to extract and apply these standards in new translation tasks. We conducted extensive tests to evaluate the effectiveness of PT, documenting the process to ensure replicability. The expected results include improved consistency and translation performance, advancing the use of AI in localization and setting a foundation for future innovation in the field.</abstract>
    <identifier type="citekey">qian-guo-2024-enhancing</identifier>
    <location>
        <url>https://aclanthology.org/2024.amta-presentations.14</url>
    </location>
    <part>
        <date>2024-09</date>
        <extent unit="page">
            <start>208</start>
            <end>221</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Enhancing Consistency Through Prompt-Tuning for Style Guide Adaptation
%A Qian, Ming
%A Guo, Zidian
%Y Martindale, Marianna
%Y Campbell, Janice
%Y Savenkov, Konstantin
%Y Goel, Shivali
%S Proceedings of the 16th Conference of the Association for Machine Translation in the Americas (Volume 2: Presentations)
%D 2024
%8 September
%I Association for Machine Translation in the Americas
%C Chicago, USA
%F qian-guo-2024-enhancing
%X This presentation explores the use of Prompt-Tuning (PT) to improve brand and language consistency in localization by teaching Large Language Models (LLMs) to develop and apply style guides from minimal examples. PT allows for the automatic enforcement of style guides for specific projects, potentially enhancing translation quality across varied tasks. Our approach involves defining key style guide components such as domain, audience, and formatting standards for acronyms, dates, and measurements, and creating prompts that instruct LLMs to extract and apply these standards in new translation tasks. We conducted extensive tests to evaluate the effectiveness of PT, documenting the process to ensure replicability. The expected results include improved consistency and translation performance, advancing the use of AI in localization and setting a foundation for future innovation in the field.
%U https://aclanthology.org/2024.amta-presentations.14
%P 208-221

Markdown (Informal)

[Enhancing Consistency Through Prompt-Tuning for Style Guide Adaptation](https://aclanthology.org/2024.amta-presentations.14) (Qian & Guo, AMTA 2024)

• Enhancing Consistency Through Prompt-Tuning for Style Guide Adaptation (Qian & Guo, AMTA 2024)

ACL

• Ming Qian and Zidian Guo. 2024. Enhancing Consistency Through Prompt-Tuning for Style Guide Adaptation. In Proceedings of the 16th Conference of the Association for Machine Translation in the Americas (Volume 2: Presentations), pages 208–221, Chicago, USA. Association for Machine Translation in the Americas.