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    <titleInfo>
        <title>Who Wrote it and Why? Prompting Large-Language Models for Authorship Verification</title>
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    <name type="personal">
        <namePart type="given">Chia-Yu</namePart>
        <namePart type="family">Hung</namePart>
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    <name type="personal">
        <namePart type="given">Zhiqiang</namePart>
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        <namePart type="given">Yujia</namePart>
        <namePart type="family">Hu</namePart>
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    <name type="personal">
        <namePart type="given">Roy</namePart>
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        <name type="personal">
            <namePart type="given">Kalika</namePart>
            <namePart type="family">Bali</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Authorship verification (AV) is a fundamental task in natural language processing (NLP) and computational linguistics, with applications in forensic analysis, plagiarism detection, and identification of deceptive content. Existing AV techniques, including traditional stylometric and deep learning approaches, face limitations in terms of data requirements and lack of explainability. To address these limitations, this paper proposes PromptAV, a novel technique that leverages Large-Language Models (LLMs) for AV by providing step-by-step stylometric explanation prompts. PromptAV outperforms state-of-the-art baselines, operates effectively with limited training data, and enhances interpretability through intuitive explanations, showcasing its potential as an effective and interpretable solution for the AV task.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.937</identifier>
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        <date>2023-12</date>
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            <start>14078</start>
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%0 Conference Proceedings
%T Who Wrote it and Why? Prompting Large-Language Models for Authorship Verification
%A Hung, Chia-Yu
%A Hu, Zhiqiang
%A Hu, Yujia
%A Lee, Roy
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F hung-etal-2023-wrote
%X Authorship verification (AV) is a fundamental task in natural language processing (NLP) and computational linguistics, with applications in forensic analysis, plagiarism detection, and identification of deceptive content. Existing AV techniques, including traditional stylometric and deep learning approaches, face limitations in terms of data requirements and lack of explainability. To address these limitations, this paper proposes PromptAV, a novel technique that leverages Large-Language Models (LLMs) for AV by providing step-by-step stylometric explanation prompts. PromptAV outperforms state-of-the-art baselines, operates effectively with limited training data, and enhances interpretability through intuitive explanations, showcasing its potential as an effective and interpretable solution for the AV task.
%R 10.18653/v1/2023.findings-emnlp.937
%U https://aclanthology.org/2023.findings-emnlp.937
%U https://doi.org/10.18653/v1/2023.findings-emnlp.937
%P 14078-14084

Markdown (Informal)

[Who Wrote it and Why? Prompting Large-Language Models for Authorship Verification](https://aclanthology.org/2023.findings-emnlp.937) (Hung et al., Findings 2023)

• Who Wrote it and Why? Prompting Large-Language Models for Authorship Verification (Hung et al., Findings 2023)

ACL

• Chia-Yu Hung, Zhiqiang Hu, Yujia Hu, and Roy Lee. 2023. Who Wrote it and Why? Prompting Large-Language Models for Authorship Verification. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 14078–14084, Singapore. Association for Computational Linguistics.