@inproceedings{pawlowski-walkowiak-2026-stylometric-approach,
    title = "Stylometric Approach to {AI}-generated Texts. An Analysis of Contemporary {F}rench-Language Literature",
    author = "Paw{\l}owski, Adam  and
      Walkowiak, Tomasz",
    editor = "Alves, Diego  and
      Bizzoni, Yuri  and
      Degaetano-Ortlieb, Stefania  and
      Kazantseva, Anna  and
      Pagel, Janis  and
      Szpakowicz, Stan",
    booktitle = "Proceedings of the 10th Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.latechclfl-1.21/",
    pages = "221--226",
    ISBN = "979-8-89176-373-9",
    abstract = "The article focuses on a stylometric analysis of authentic literary texts and thematically related texts generated by large language models. The texts under study represent a fairly broad cross-section of twentieth-century French literature. Five models were used to generate the texts (ChatGPT 4-o, GPT 4-o mini, DeepSeek v.3, c4ai-command-r-plus, and c4ai-command-a). The original human-written stories of approximately 20,000 characters were summarized, and new narratives were then generated on the basis of these abstracts. In terms of plot and style, they were intended to resemble the originals. The research carried out with TF-IDF of the most frequent words showed that texts generated by specific LLMs and written by humans cluster relatively well as distinct groups. The experiments also showed that the ``authorial'' specificity of machine-generated texts partly matches the original clustering of human-written source texts."
}