<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="dwivedi-etal-2025-eticor">
    <titleInfo>
        <title>EtiCor++: Towards Understanding Etiquettical Bias in LLMs</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Ashutosh</namePart>
        <namePart type="family">Dwivedi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Siddhant</namePart>
        <namePart type="given">Shivdutt</namePart>
        <namePart type="family">Singh</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ashutosh</namePart>
        <namePart type="family">Modi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2025-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: ACL 2025</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Wanxiang</namePart>
            <namePart type="family">Che</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Joyce</namePart>
            <namePart type="family">Nabende</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Shutova</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mohammad</namePart>
            <namePart type="given">Taher</namePart>
            <namePart type="family">Pilehvar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Vienna, Austria</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-256-5</identifier>
    </relatedItem>
    <abstract>In recent years, researchers have started analyzing the cultural sensitivity of LLMs. In this respect, Etiquettes have been an active area of research. Etiquettes are region-specific and are an essential part of the culture of a region; hence, it is imperative to make LLMs sensitive to etiquettes. However, there needs to be more resources in evaluating LLMs for their understanding and bias with regard to etiquettes. In this resource paper, we introduce EtiCor++, a corpus of etiquettes worldwide. We introduce different tasks for evaluating LLMs for knowledge about etiquettes across various regions. Further, we introduce various metrics for measuring bias in LLMs. Extensive experimentation with LLMs shows inherent bias towards certain regions.</abstract>
    <identifier type="citekey">dwivedi-etal-2025-eticor</identifier>
    <identifier type="doi">10.18653/v1/2025.findings-acl.488</identifier>
    <location>
        <url>https://aclanthology.org/2025.findings-acl.488/</url>
    </location>
    <part>
        <date>2025-07</date>
        <extent unit="page">
            <start>9355</start>
            <end>9376</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T EtiCor++: Towards Understanding Etiquettical Bias in LLMs
%A Dwivedi, Ashutosh
%A Singh, Siddhant Shivdutt
%A Modi, Ashutosh
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F dwivedi-etal-2025-eticor
%X In recent years, researchers have started analyzing the cultural sensitivity of LLMs. In this respect, Etiquettes have been an active area of research. Etiquettes are region-specific and are an essential part of the culture of a region; hence, it is imperative to make LLMs sensitive to etiquettes. However, there needs to be more resources in evaluating LLMs for their understanding and bias with regard to etiquettes. In this resource paper, we introduce EtiCor++, a corpus of etiquettes worldwide. We introduce different tasks for evaluating LLMs for knowledge about etiquettes across various regions. Further, we introduce various metrics for measuring bias in LLMs. Extensive experimentation with LLMs shows inherent bias towards certain regions.
%R 10.18653/v1/2025.findings-acl.488
%U https://aclanthology.org/2025.findings-acl.488/
%U https://doi.org/10.18653/v1/2025.findings-acl.488
%P 9355-9376

Markdown (Informal)

[EtiCor++: Towards Understanding Etiquettical Bias in LLMs](https://aclanthology.org/2025.findings-acl.488/) (Dwivedi et al., Findings 2025)

• EtiCor++: Towards Understanding Etiquettical Bias in LLMs (Dwivedi et al., Findings 2025)

ACL

• Ashutosh Dwivedi, Siddhant Shivdutt Singh, and Ashutosh Modi. 2025. EtiCor++: Towards Understanding Etiquettical Bias in LLMs. In Findings of the Association for Computational Linguistics: ACL 2025, pages 9355–9376, Vienna, Austria. Association for Computational Linguistics.