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        <title>Aligning Large Language Models with Diverse Political Viewpoints</title>
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    <name type="personal">
        <namePart type="given">Dominik</namePart>
        <namePart type="family">Stammbach</namePart>
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    <name type="personal">
        <namePart type="given">Philine</namePart>
        <namePart type="family">Widmer</namePart>
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    <name type="personal">
        <namePart type="given">Eunjung</namePart>
        <namePart type="family">Cho</namePart>
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    <name type="personal">
        <namePart type="given">Caglar</namePart>
        <namePart type="family">Gulcehre</namePart>
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    <name type="personal">
        <namePart type="given">Elliott</namePart>
        <namePart type="family">Ash</namePart>
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            <title>Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Yaser</namePart>
            <namePart type="family">Al-Onaizan</namePart>
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        <name type="personal">
            <namePart type="given">Mohit</namePart>
            <namePart type="family">Bansal</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Yun-Nung</namePart>
            <namePart type="family">Chen</namePart>
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    <abstract>Large language models such as ChatGPT exhibit striking political biases. If users query them about political information, they often take a normative stance. To overcome this, we align LLMs with diverse political viewpoints from 100,000 comments written by candidates running for national parliament in Switzerland. Models aligned with this data can generate more accurate political viewpoints from Swiss parties, compared to commercial models such as ChatGPT. We also propose a procedure to generate balanced overviews summarizing multiple viewpoints using such models. The replication package contains all code and data.</abstract>
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    <identifier type="doi">10.18653/v1/2024.emnlp-main.412</identifier>
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        <date>2024-11</date>
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%0 Conference Proceedings
%T Aligning Large Language Models with Diverse Political Viewpoints
%A Stammbach, Dominik
%A Widmer, Philine
%A Cho, Eunjung
%A Gulcehre, Caglar
%A Ash, Elliott
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F stammbach-etal-2024-aligning
%X Large language models such as ChatGPT exhibit striking political biases. If users query them about political information, they often take a normative stance. To overcome this, we align LLMs with diverse political viewpoints from 100,000 comments written by candidates running for national parliament in Switzerland. Models aligned with this data can generate more accurate political viewpoints from Swiss parties, compared to commercial models such as ChatGPT. We also propose a procedure to generate balanced overviews summarizing multiple viewpoints using such models. The replication package contains all code and data.
%R 10.18653/v1/2024.emnlp-main.412
%U https://aclanthology.org/2024.emnlp-main.412
%U https://doi.org/10.18653/v1/2024.emnlp-main.412
%P 7257-7267

Markdown (Informal)

[Aligning Large Language Models with Diverse Political Viewpoints](https://aclanthology.org/2024.emnlp-main.412) (Stammbach et al., EMNLP 2024)

• Aligning Large Language Models with Diverse Political Viewpoints (Stammbach et al., EMNLP 2024)

ACL

• Dominik Stammbach, Philine Widmer, Eunjung Cho, Caglar Gulcehre, and Elliott Ash. 2024. Aligning Large Language Models with Diverse Political Viewpoints. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 7257–7267, Miami, Florida, USA. Association for Computational Linguistics.