<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="yoo-etal-2024-advancing">
    <titleInfo>
        <title>Advancing Beyond Identification: Multi-bit Watermark for Large Language Models</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">KiYoon</namePart>
        <namePart type="family">Yoo</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Wonhyuk</namePart>
        <namePart type="family">Ahn</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Nojun</namePart>
        <namePart type="family">Kwak</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Kevin</namePart>
            <namePart type="family">Duh</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Helena</namePart>
            <namePart type="family">Gomez</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Steven</namePart>
            <namePart type="family">Bethard</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Mexico City, Mexico</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We show the viability of tackling misuses of large language models beyond the identification of machine-generated text. While existing zero-bit watermark methods focus on detection only, some malicious misuses demand tracing the adversary user for counteracting them. To address this, we propose Multi-bit Watermark via Position Allocation, embedding traceable multi-bit information during language model generation. Through allocating tokens onto different parts of the messages, we embed longer messages in high corruption settings without added latency. By independently embedding sub-units of messages, the proposed method outperforms the existing works in terms of robustness and latency. Leveraging the benefits of zero-bit watermarking, our method enables robust extraction of the watermark without any model access, embedding and extraction of long messages (\geq 32-bit) without finetuning, and maintaining text quality, while allowing zero-bit detection all at the same time.</abstract>
    <identifier type="citekey">yoo-etal-2024-advancing</identifier>
    <location>
        <url>https://aclanthology.org/2024.naacl-long.224</url>
    </location>
    <part>
        <date>2024-06</date>
        <extent unit="page">
            <start>4031</start>
            <end>4055</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Advancing Beyond Identification: Multi-bit Watermark for Large Language Models
%A Yoo, KiYoon
%A Ahn, Wonhyuk
%A Kwak, Nojun
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F yoo-etal-2024-advancing
%X We show the viability of tackling misuses of large language models beyond the identification of machine-generated text. While existing zero-bit watermark methods focus on detection only, some malicious misuses demand tracing the adversary user for counteracting them. To address this, we propose Multi-bit Watermark via Position Allocation, embedding traceable multi-bit information during language model generation. Through allocating tokens onto different parts of the messages, we embed longer messages in high corruption settings without added latency. By independently embedding sub-units of messages, the proposed method outperforms the existing works in terms of robustness and latency. Leveraging the benefits of zero-bit watermarking, our method enables robust extraction of the watermark without any model access, embedding and extraction of long messages (\geq 32-bit) without finetuning, and maintaining text quality, while allowing zero-bit detection all at the same time.
%U https://aclanthology.org/2024.naacl-long.224
%P 4031-4055

Markdown (Informal)

[Advancing Beyond Identification: Multi-bit Watermark for Large Language Models](https://aclanthology.org/2024.naacl-long.224) (Yoo et al., NAACL 2024)

• Advancing Beyond Identification: Multi-bit Watermark for Large Language Models (Yoo et al., NAACL 2024)

ACL

• KiYoon Yoo, Wonhyuk Ahn, and Nojun Kwak. 2024. Advancing Beyond Identification: Multi-bit Watermark for Large Language Models. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 4031–4055, Mexico City, Mexico. Association for Computational Linguistics.