<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wu-ebling-2024-investigating">
    <titleInfo>
        <title>Investigating Ableism in LLMs through Multi-turn Conversation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Guojun</namePart>
        <namePart type="family">Wu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sarah</namePart>
        <namePart type="family">Ebling</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Third Workshop on NLP for Positive Impact</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Daryna</namePart>
            <namePart type="family">Dementieva</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Oana</namePart>
            <namePart type="family">Ignat</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Zhijing</namePart>
            <namePart type="family">Jin</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Rada</namePart>
            <namePart type="family">Mihalcea</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Giorgio</namePart>
            <namePart type="family">Piatti</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Joel</namePart>
            <namePart type="family">Tetreault</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Steven</namePart>
            <namePart type="family">Wilson</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jieyu</namePart>
            <namePart type="family">Zhao</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Miami, Florida, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>To reveal ableism (i.e., bias against persons with disabilities) in large language models (LLMs), we introduce a novel approach involving multi-turn conversations, enabling a comparative assessment. Initially, we prompt the LLM to elaborate short biographies, followed by a request to incorporate information about a disability. Finally, we employ several methods to identify the top words that distinguish the disability-integrated biographies from those without. This comparative setting helps us uncover how LLMs handle disability-related information and reveal underlying biases. We observe that LLMs tend to highlight disabilities in a manner that can be perceived as patronizing or as implying that overcoming challenges is unexpected due to the disability.</abstract>
    <identifier type="citekey">wu-ebling-2024-investigating</identifier>
    <location>
        <url>https://aclanthology.org/2024.nlp4pi-1.18</url>
    </location>
    <part>
        <date>2024-11</date>
        <extent unit="page">
            <start>202</start>
            <end>210</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Investigating Ableism in LLMs through Multi-turn Conversation
%A Wu, Guojun
%A Ebling, Sarah
%Y Dementieva, Daryna
%Y Ignat, Oana
%Y Jin, Zhijing
%Y Mihalcea, Rada
%Y Piatti, Giorgio
%Y Tetreault, Joel
%Y Wilson, Steven
%Y Zhao, Jieyu
%S Proceedings of the Third Workshop on NLP for Positive Impact
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F wu-ebling-2024-investigating
%X To reveal ableism (i.e., bias against persons with disabilities) in large language models (LLMs), we introduce a novel approach involving multi-turn conversations, enabling a comparative assessment. Initially, we prompt the LLM to elaborate short biographies, followed by a request to incorporate information about a disability. Finally, we employ several methods to identify the top words that distinguish the disability-integrated biographies from those without. This comparative setting helps us uncover how LLMs handle disability-related information and reveal underlying biases. We observe that LLMs tend to highlight disabilities in a manner that can be perceived as patronizing or as implying that overcoming challenges is unexpected due to the disability.
%U https://aclanthology.org/2024.nlp4pi-1.18
%P 202-210

Markdown (Informal)

[Investigating Ableism in LLMs through Multi-turn Conversation](https://aclanthology.org/2024.nlp4pi-1.18) (Wu & Ebling, NLP4PI 2024)

• Investigating Ableism in LLMs through Multi-turn Conversation (Wu & Ebling, NLP4PI 2024)

ACL

• Guojun Wu and Sarah Ebling. 2024. Investigating Ableism in LLMs through Multi-turn Conversation. In Proceedings of the Third Workshop on NLP for Positive Impact, pages 202–210, Miami, Florida, USA. Association for Computational Linguistics.