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    <titleInfo>
        <title>CAVA: A Tool for Cultural Alignment Visualization &amp; Analysis</title>
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    <name type="personal">
        <namePart type="given">Nevan</namePart>
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        <namePart type="given">Cheng</namePart>
        <namePart type="given">Charles</namePart>
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    <name type="personal">
        <namePart type="given">Prakruthi</namePart>
        <namePart type="family">Pradeep</namePart>
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    <name type="personal">
        <namePart type="given">Daphne</namePart>
        <namePart type="family">Ippolito</namePart>
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            <title>Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations</title>
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        <name type="personal">
            <namePart type="given">Delia</namePart>
            <namePart type="given">Irazu</namePart>
            <namePart type="family">Hernandez Farias</namePart>
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            <namePart type="given">Manling</namePart>
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    <abstract>It is well-known that language models are biased; they have patchy knowledge of countries and cultures that are poorly represented in their training data. We introduce CAVA, a visualization tool for identifying and analyzing country-specific biases in language models.Our tool allows users to identify whether a language model successful captures the perspectives of people of different nationalities. The tool supports analysis of both longform and multiple-choice models responses and comparisons between models.Our open-source code easily allows users to upload any country-based language model generations they wish to analyze.To showcase CAVA’s efficacy, we present a case study analyzing how several popular language models answer survey questions from the World Values Survey.</abstract>
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        <url>https://aclanthology.org/2024.emnlp-demo.16</url>
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        <date>2024-11</date>
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            <start>153</start>
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%0 Conference Proceedings
%T CAVA: A Tool for Cultural Alignment Visualization & Analysis
%A Giuliani, Nevan
%A Ma, Cheng Charles
%A Pradeep, Prakruthi
%A Ippolito, Daphne
%Y Hernandez Farias, Delia Irazu
%Y Hope, Tom
%Y Li, Manling
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F giuliani-etal-2024-cava
%X It is well-known that language models are biased; they have patchy knowledge of countries and cultures that are poorly represented in their training data. We introduce CAVA, a visualization tool for identifying and analyzing country-specific biases in language models.Our tool allows users to identify whether a language model successful captures the perspectives of people of different nationalities. The tool supports analysis of both longform and multiple-choice models responses and comparisons between models.Our open-source code easily allows users to upload any country-based language model generations they wish to analyze.To showcase CAVA’s efficacy, we present a case study analyzing how several popular language models answer survey questions from the World Values Survey.
%U https://aclanthology.org/2024.emnlp-demo.16
%P 153-161

Markdown (Informal)

[CAVA: A Tool for Cultural Alignment Visualization & Analysis](https://aclanthology.org/2024.emnlp-demo.16) (Giuliani et al., EMNLP 2024)

• CAVA: A Tool for Cultural Alignment Visualization & Analysis (Giuliani et al., EMNLP 2024)

ACL

• Nevan Giuliani, Cheng Charles Ma, Prakruthi Pradeep, and Daphne Ippolito. 2024. CAVA: A Tool for Cultural Alignment Visualization & Analysis. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 153–161, Miami, Florida, USA. Association for Computational Linguistics.