@inproceedings{alshomary-etal-2025-layered,
    title = "Layered Insights: Generalizable Analysis of Human Authorial Style by Leveraging All Transformer Layers",
    author = "Alshomary, Milad  and
      Varimalla, Nikhil Reddy  and
      Anand, Vishal  and
      Muresan, Smaranda  and
      McKeown, Kathleen",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.521/",
    pages = "10290--10303",
    ISBN = "979-8-89176-332-6",
    abstract = "We propose a new approach for the authorship attribution task that leverages the various linguistic representations learned at different layers of pre-trained transformer-based models. We evaluate our approach on two popular authorship attribution models and three evaluation datasets, in in-domain and out-of-domain scenarios. We found that utilizing various transformer layers improves the robustness of authorship attribution models when tested on out-of-domain data, resulting in a much stronger performance. Our analysis gives further insights into how our model{'}s different layers get specialized in representing certain linguistic aspects that we believe benefit the model when tested out of the domain."
}