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    <titleInfo>
        <title>Conformal Prediction for Natural Language Processing: A Survey</title>
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    <name type="personal">
        <namePart type="given">Margarida</namePart>
        <namePart type="family">Campos</namePart>
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        <namePart type="given">António</namePart>
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        <namePart type="given">Mário</namePart>
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    <abstract>The rapid proliferation of large language models and natural language processing (NLP) applications creates a crucial need for uncertainty quantification to mitigate risks such as Hallucinations and to enhance decision-making reliability in critical applications. Conformal prediction is emerging as a theoretically sound and practically useful framework, combining flexibility with strong statistical guarantees. Its model-agnostic and distribution-free nature makes it particularly promising to address the current shortcomings of NLP systems that stem from the absence of uncertainty quantification. This paper provides a comprehensive survey of conformal prediction techniques, their guarantees, and existing applications in NLP, pointing to directions for future research and open challenges.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00715</identifier>
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        <url>https://aclanthology.org/2024.tacl-1.82/</url>
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        <date>2024</date>
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            <start>1497</start>
            <end>1516</end>
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%0 Journal Article
%T Conformal Prediction for Natural Language Processing: A Survey
%A Campos, Margarida
%A Farinhas, António
%A Zerva, Chrysoula
%A Figueiredo, Mário A. T.
%A Martins, André F. T.
%J Transactions of the Association for Computational Linguistics
%D 2024
%V 12
%I MIT Press
%C Cambridge, MA
%F campos-etal-2024-conformal
%X The rapid proliferation of large language models and natural language processing (NLP) applications creates a crucial need for uncertainty quantification to mitigate risks such as Hallucinations and to enhance decision-making reliability in critical applications. Conformal prediction is emerging as a theoretically sound and practically useful framework, combining flexibility with strong statistical guarantees. Its model-agnostic and distribution-free nature makes it particularly promising to address the current shortcomings of NLP systems that stem from the absence of uncertainty quantification. This paper provides a comprehensive survey of conformal prediction techniques, their guarantees, and existing applications in NLP, pointing to directions for future research and open challenges.
%R 10.1162/tacl_a_00715
%U https://aclanthology.org/2024.tacl-1.82/
%U https://doi.org/10.1162/tacl_a_00715
%P 1497-1516

Markdown (Informal)

[Conformal Prediction for Natural Language Processing: A Survey](https://aclanthology.org/2024.tacl-1.82/) (Campos et al., TACL 2024)

• Conformal Prediction for Natural Language Processing: A Survey (Campos et al., TACL 2024)

ACL

• Margarida Campos, António Farinhas, Chrysoula Zerva, Mário A. T. Figueiredo, and André F. T. Martins. 2024. Conformal Prediction for Natural Language Processing: A Survey. Transactions of the Association for Computational Linguistics, 12:1497–1516.