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    <titleInfo>
        <title>Measuring Political Bias in Large Language Models: What Is Said and How It Is Said</title>
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        <namePart type="given">Yejin</namePart>
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    <name type="personal">
        <namePart type="given">Delong</namePart>
        <namePart type="family">Chen</namePart>
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        <namePart type="given">Pascale</namePart>
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            <title>Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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    <abstract>We propose to measure political bias in LLMs by analyzing both the content and style of their generated content regarding political issues. Existing benchmarks and measures focus on gender and racial biases. However, political bias exists in LLMs and can lead to polarization and other harms in downstream applications. In order to provide transparency to users, we advocate that there should be fine-grained and explainable measures of political biases generated by LLMs. Our proposed measure looks at different political issues such as reproductive rights and climate change, at both the content (the substance of the generation) and the style (the lexical polarity) of such bias. We measured the political bias in eleven open-sourced LLMs and showed that our proposed framework is easily scalable to other topics and is explainable.</abstract>
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    <identifier type="doi">10.18653/v1/2024.acl-long.600</identifier>
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        <date>2024-08</date>
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%0 Conference Proceedings
%T Measuring Political Bias in Large Language Models: What Is Said and How It Is Said
%A Bang, Yejin
%A Chen, Delong
%A Lee, Nayeon
%A Fung, Pascale
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F bang-etal-2024-measuring
%X We propose to measure political bias in LLMs by analyzing both the content and style of their generated content regarding political issues. Existing benchmarks and measures focus on gender and racial biases. However, political bias exists in LLMs and can lead to polarization and other harms in downstream applications. In order to provide transparency to users, we advocate that there should be fine-grained and explainable measures of political biases generated by LLMs. Our proposed measure looks at different political issues such as reproductive rights and climate change, at both the content (the substance of the generation) and the style (the lexical polarity) of such bias. We measured the political bias in eleven open-sourced LLMs and showed that our proposed framework is easily scalable to other topics and is explainable.
%R 10.18653/v1/2024.acl-long.600
%U https://aclanthology.org/2024.luhme-long.600/
%U https://doi.org/10.18653/v1/2024.acl-long.600
%P 11142-11159

Markdown (Informal)

[Measuring Political Bias in Large Language Models: What Is Said and How It Is Said](https://aclanthology.org/2024.luhme-long.600/) (Bang et al., ACL 2024)

• Measuring Political Bias in Large Language Models: What Is Said and How It Is Said (Bang et al., ACL 2024)

ACL

• Yejin Bang, Delong Chen, Nayeon Lee, and Pascale Fung. 2024. Measuring Political Bias in Large Language Models: What Is Said and How It Is Said. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11142–11159, Bangkok, Thailand. Association for Computational Linguistics.