@inproceedings{gorovaia-etal-2024-sui,
    title = "{S}ui Generis: Large Language Models for Authorship Attribution and Verification in {L}atin",
    author = "Gorovaia, Svetlana  and
      Schmidt, Gleb  and
      Yamshchikov, Ivan P.",
    editor = {H{\"a}m{\"a}l{\"a}inen, Mika  and
      {\"O}hman, Emily  and
      Miyagawa, So  and
      Alnajjar, Khalid  and
      Bizzoni, Yuri},
    booktitle = "Proceedings of the 4th International Conference on Natural Language Processing for Digital Humanities",
    month = nov,
    year = "2024",
    address = "Miami, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.nlp4dh-1.39/",
    doi = "10.18653/v1/2024.nlp4dh-1.39",
    pages = "398--412",
    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 {\textquotedblleft}mislead{\textquotedblright} 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."
}