<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="xu-ding-2025-large">
    <titleInfo>
        <title>Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Ruiyao</namePart>
        <namePart type="family">Xu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Kaize</namePart>
        <namePart type="family">Ding</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2025-04</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: NAACL 2025</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Luis</namePart>
            <namePart type="family">Chiruzzo</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Alan</namePart>
            <namePart type="family">Ritter</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Lu</namePart>
            <namePart type="family">Wang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Albuquerque, New Mexico</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-195-7</identifier>
    </relatedItem>
    <abstract>Detecting anomalies or out-of-distribution (OOD) samples is critical for maintaining the reliability and trustworthiness of machine learning systems. Recently, Large Language Models (LLMs) have demonstrated their effectiveness not only in natural language processing but also in broader applications due to their advanced comprehension and generative capabilities. The integration of LLMs into anomaly and OOD detection marks a significant shift from the traditional paradigm in the field. This survey focuses on the problem of anomaly and OOD detection under the context of LLMs. We propose a new taxonomy to categorize existing approaches into two classes based on the role played by LLMs. Following our proposed taxonomy, we further discuss the related work under each of the categories and finally discuss potential challenges and directions for future research in this field. We also provide an up-to-date reading list of relevant papers: https://github.com/rux001/Awesome-LLM-Anomaly-OOD-Detection.</abstract>
    <identifier type="citekey">xu-ding-2025-large</identifier>
    <identifier type="doi">10.18653/v1/2025.findings-naacl.333</identifier>
    <location>
        <url>https://aclanthology.org/2025.findings-naacl.333/</url>
    </location>
    <part>
        <date>2025-04</date>
        <extent unit="page">
            <start>5992</start>
            <end>6012</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey
%A Xu, Ruiyao
%A Ding, Kaize
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F xu-ding-2025-large
%X Detecting anomalies or out-of-distribution (OOD) samples is critical for maintaining the reliability and trustworthiness of machine learning systems. Recently, Large Language Models (LLMs) have demonstrated their effectiveness not only in natural language processing but also in broader applications due to their advanced comprehension and generative capabilities. The integration of LLMs into anomaly and OOD detection marks a significant shift from the traditional paradigm in the field. This survey focuses on the problem of anomaly and OOD detection under the context of LLMs. We propose a new taxonomy to categorize existing approaches into two classes based on the role played by LLMs. Following our proposed taxonomy, we further discuss the related work under each of the categories and finally discuss potential challenges and directions for future research in this field. We also provide an up-to-date reading list of relevant papers: https://github.com/rux001/Awesome-LLM-Anomaly-OOD-Detection.
%R 10.18653/v1/2025.findings-naacl.333
%U https://aclanthology.org/2025.findings-naacl.333/
%U https://doi.org/10.18653/v1/2025.findings-naacl.333
%P 5992-6012

Markdown (Informal)

[Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey](https://aclanthology.org/2025.findings-naacl.333/) (Xu & Ding, Findings 2025)

• Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey (Xu & Ding, Findings 2025)

ACL

• Ruiyao Xu and Kaize Ding. 2025. Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 5992–6012, Albuquerque, New Mexico. Association for Computational Linguistics.