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        <title>Advancing LLM detection in the ALTA 2024 Shared Task: Techniques and Analysis</title>
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            <title>Proceedings of the 22nd Annual Workshop of the Australasian Language Technology Association</title>
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            <namePart type="given">Tim</namePart>
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            <namePart type="given">Sergio</namePart>
            <namePart type="given">José</namePart>
            <namePart type="family">Rodríguez Méndez</namePart>
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    <abstract>The recent proliferation of AI-generated content has prompted significant interest in developing reliable detection methods. This study explores techniques for identifying AIgenerated text through sentence-level evaluation within hybrid articles. Our findings indicate that ChatGPT-3.5 Turbo exhibits distinct, repetitive probability patterns that enable consistent in-domain detection. Empirical tests show that minor textual modifications, such as rewording, have minimal impact on detection accuracy. These results provide valuable insights for advancing AI detection methodologies, offering a pathway toward robust solutions to address the complexities of synthetic text identification.</abstract>
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%0 Conference Proceedings
%T Advancing LLM detection in the ALTA 2024 Shared Task: Techniques and Analysis
%A Galat, Dima
%Y Baldwin, Tim
%Y Rodríguez Méndez, Sergio José
%Y Kuo, Nicholas
%S Proceedings of the 22nd Annual Workshop of the Australasian Language Technology Association
%D 2024
%8 December
%I Association for Computational Linguistics
%C Canberra, Australia
%F galat-2024-advancing
%X The recent proliferation of AI-generated content has prompted significant interest in developing reliable detection methods. This study explores techniques for identifying AIgenerated text through sentence-level evaluation within hybrid articles. Our findings indicate that ChatGPT-3.5 Turbo exhibits distinct, repetitive probability patterns that enable consistent in-domain detection. Empirical tests show that minor textual modifications, such as rewording, have minimal impact on detection accuracy. These results provide valuable insights for advancing AI detection methodologies, offering a pathway toward robust solutions to address the complexities of synthetic text identification.
%U https://aclanthology.org/2024.alta-1.18/
%P 203-206

Markdown (Informal)

[Advancing LLM detection in the ALTA 2024 Shared Task: Techniques and Analysis](https://aclanthology.org/2024.alta-1.18/) (Galat, ALTA 2024)

• Advancing LLM detection in the ALTA 2024 Shared Task: Techniques and Analysis (Galat, ALTA 2024)

ACL

• Dima Galat. 2024. Advancing LLM detection in the ALTA 2024 Shared Task: Techniques and Analysis. In Proceedings of the 22nd Annual Workshop of the Australasian Language Technology Association, pages 203–206, Canberra, Australia. Association for Computational Linguistics.