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        <title>Sui Generis: Large Language Models for Authorship Attribution and Verification in Latin</title>
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    <name type="personal">
        <namePart type="given">Svetlana</namePart>
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            <namePart type="given">Mika</namePart>
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    <abstract>This paper evaluates the performance of Large Language Models (LLMs) in authorship attribu- tion and authorship verification tasks for Latin texts of the Patristic Era. The study showcases that LLMs can be robust in zero-shot author- ship verification even on short texts without sophisticated feature engineering. Yet, the mod- els can also be easily “mislead” by semantics. The experiments also demonstrate that steering the model’s authorship analysis and decision- making is challenging, unlike what is reported in the studies dealing with high-resource mod- ern languages. Although LLMs prove to be able to beat, under certain circumstances, the traditional baselines, obtaining a nuanced and truly explainable decision requires at best a lot of experimentation.</abstract>
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%0 Conference Proceedings
%T Sui Generis: Large Language Models for Authorship Attribution and Verification in Latin
%A Gorovaia, Svetlana
%A Schmidt, Gleb
%A Yamshchikov, Ivan P.
%Y Hämäläinen, Mika
%Y Öhman, Emily
%Y Miyagawa, So
%Y Alnajjar, Khalid
%Y Bizzoni, Yuri
%S Proceedings of the 4th International Conference on Natural Language Processing for Digital Humanities
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, USA
%F gorovaia-etal-2024-sui
%X This paper evaluates the performance of Large Language Models (LLMs) in authorship attribu- tion and authorship verification tasks for Latin texts of the Patristic Era. The study showcases that LLMs can be robust in zero-shot author- ship verification even on short texts without sophisticated feature engineering. Yet, the mod- els can also be easily “mislead” by semantics. The experiments also demonstrate that steering the model’s authorship analysis and decision- making is challenging, unlike what is reported in the studies dealing with high-resource mod- ern languages. Although LLMs prove to be able to beat, under certain circumstances, the traditional baselines, obtaining a nuanced and truly explainable decision requires at best a lot of experimentation.
%U https://aclanthology.org/2024.nlp4dh-1.39
%P 398-412

Markdown (Informal)

[Sui Generis: Large Language Models for Authorship Attribution and Verification in Latin](https://aclanthology.org/2024.nlp4dh-1.39) (Gorovaia et al., NLP4DH 2024)

• Sui Generis: Large Language Models for Authorship Attribution and Verification in Latin (Gorovaia et al., NLP4DH 2024)

ACL

• Svetlana Gorovaia, Gleb Schmidt, and Ivan P. Yamshchikov. 2024. Sui Generis: Large Language Models for Authorship Attribution and Verification in Latin. In Proceedings of the 4th International Conference on Natural Language Processing for Digital Humanities, pages 398–412, Miami, USA. Association for Computational Linguistics.